Procuring Pediatric Vaccines in a Two-Economy Duopoly

In this work, we aim to present an optimization model for vaccine pricing in a two-economy duopoly. This model observes the price dynamics between a high income country and a low income country that procure vaccinations through PAHO. This model is formulated to provide insights on optimal pricing strategy for PAHO to ultimately increase vaccine accessibility to low income countries. The objective is to satisfy the public demand at the lowest price possible, while providing enough profit for the vaccine manufacturers to stay in business. Using non-linear integer programming, the model results show that cross-subsidization occurs in PAHO vaccine procurement

Image-to-Image Retrieval by Learning Similarity between Scene Graphs

As a scene graph compactly summarizes the high-level content of an image in a structured and symbolic manner, the similarity between scene graphs of two images reflects the relevance of their contents. Based on this idea, we propose a novel approach for image-to-image retrieval using scene graph similarity measured by graph neural networks. In our approach, graph neural networks are trained to predict the proxy image relevance measure, computed from human-annotated captions using a pre-trained sentence similarity model. We collect and publish the dataset for image relevance measured by human annotators to evaluate retrieval algorithms. The collected dataset shows that our method agrees well with the human perception of image similarity than other competitive baselines.Comment: Accepted to AAAI 202

Cross-Regulation between Oncogenic BRAFV600E Kinase and the MST1 Pathway in Papillary Thyroid Carcinoma

BACKGROUND:The BRAF(V600E) mutation leading to constitutive signaling of MEK-ERK pathways causes papillary thyroid cancer (PTC). Ras association domain family 1A (RASSF1A), which is an important regulator of MST1 tumor suppressor pathways, is inactivated by hypermethylation of its promoter region in 20 to 32% of PTC. However, in PTC without RASSF1A methylation, the regulatory mechanisms of RASSF1A-MST1 pathways remain to be elucidated, and the functional cooperation or cross regulation between BRAF(V600E) and MST1,which activates Foxo3,has not been investigated. METHODOLOGY/PRINCIPAL FINDINGS:The negative regulators of the cell cycle, p21 and p27, are strongly induced by transcriptional activation of FoxO3 in BRAF(V600E) positive thyroid cancer cells. The FoxO3 transactivation is augmented by RASSF1A and the MST1 signaling pathway. Interestingly, introduction of BRAF(V600E)markedly abolished FoxO3 transactivation and resulted in the suppression of p21 and p27 expression. The suppression of FoxO3 transactivation by BRAF(V600E)is strongly increased by coexpression of MST1 but it is not observed in the cells in which MST1, but not MST2,is silenced. Mechanistically, BRAF(V600E)was able to bind to the C-terminal region of MST1 and resulted in the suppression of MST1 kinase activities. The induction of the G1-checkpoint CDK inhibitors, p21 and p27,by the RASSF1A-MST1-FoxO3 pathway facilitates cellular apoptosis, whereas addition of BRAF(V600E) inhibits the apoptotic processes through the inactivation of MST1. Transgenic induction of BRAF(V600E)in the thyroid gland results in cancers resembling human papillary thyroid cancers. The development of BRAF(V600E)transgenic mice with the MST1 knockout background showed that these mice had abundant foci of poorly differentiated carcinomas and large areas without follicular architecture or colloid formation. CONCLUSIONS/SIGNIFICANCE:The results of this study revealed that the oncogenic effect of BRAF(V600E) is associated with the inhibition of MST1 tumor suppressor pathways, and that the activity of RASSF1A-MST1-FoxO3 pathways determines the phenotypes of BRAF(V600E) tumors

Effects of gas saturation and sparging on sonochemical oxidation activity under different liquid level and volume conditions in 300-kHz sonoreactors: Zeroth- and first-order reaction comparison using KI dosimetry and BPA degradation

The sonochemical oxidation activity was investigated for gas saturation and gas sparging under various liquid levels and volumes in 300 kHz sonoreactors. The liquid levels and volumes ranged from 5λ (25 mm, 0.47 L) to 50λ (250 mm, 4.30 L) and two gas mixtures, Ar:O2 (75:25) and N2:O2 (75:25), were used. Two types of reaction kinetics were observed to quantitatively analyze the sonochemical oxidation reactions: zero-order (KI dosimetry: C0 = 60.2 mM) and first-order (Bisphenol A (BPA) degradation: C0 = 0.043 mM). The masses of the sonochemical oxidation reactions were calculated and compared rather than the concentrations to more accurately compare the sonochemical oxidation activity under different liquid volume conditions. First, as the liquid level or volume increased for the zero-order reactions, the concentration of I3- ions representing the volume-averaged activity decreased substantially for gas saturation owing to the increase in liquid volume. However, gas sparging substantially enhanced sonochemical oxidation activity, and the mass of I3- ions representing the total activity remained constant as the liquid level increased from 20λ because of the improved liquid mixing and a shift in the sonochemical active zone. Second, as evidenced by the zero-order reactions, the concentration of BPA decreased considerably as the liquid level or volume increased in the first-order reactions. When gas sparging was used, higher reaction constants were obtained for both gas mixtures, ranging from 40λ to 50λ. However, a comparison of the sonochemical oxidation activity in terms of the degraded mass of BPA was inapplicable as the concentration of BPA decreased substantially and a lack of reactants occurred for the lower liquid level and volume conditions as the irradiation time elapsed. Instead, using the first-order reaction constant, a comparison of the required reaction times for a specific removal efficiency (30%, 60%, and 90%) was proposed. Gas sparging can substantially reduce the reaction time required for a liquid level of 40λ or higher

Identification of long-standing and emerging agendas in international forest policy discourse

This study examined forest policy agendas developed in international policy-making process by analyzing international forest policy documents from 2001 to 2022 with power, perception, potency, and proximity. The forest policy agendas consistently addressed in the documents were agroforestry, biodiversity, climate change, certification, desertification, deforestation, forest landscape restoration, illegal logging and trade, non-timber forest products, sustainable forest management, traditional knowledge, governance, participation, partnerships, forest tenure, forest fire, forest disease, and community-based forest management. The emerging agendas since 2011 were ecosystem services, reducing emissions from deforestation and forest degradation plus (REDD+), resilience, urban forests, green economy/bioeconomy, and COVID-19. The changes in international forest policy discourse with long-standing and emerging agendas over time showed three characteristics: policy coherence by the power of international environmental conventions; expansion of forest policy targets and areas by perception and proximity of urban forests; and innovative approaches to resilience and bioeconomy by potency and perception. Therefore, this study offers new insights into the creation and transitions of forest policy agendas in the international forest policy discourse

Correlation Analysis of Noise, Vibration, and Harshness in a Vehicle Using Driving Data Based on Big Data Analysis Technique

A new development process for the noise, vibration, and harshness (NVH) of a vehicle is presented using data analysis and machine learning with long-term NVH driving data. The process includes exploratory data analysis (EDA), variable importance analysis, correlation analysis, sensitivity analysis, and development target selection. In this paper, to dramatically reduce the development period and cost related to vehicle NVH, we propose a technique that can accurately identify the precise connectivity and relationship between vehicle systems and NVH factors. This new technique uses whole big data and reflects the nonlinearity of dynamic characteristics, which was not considered in existing methods, and no data are discarded. Through the proposed method, it is possible to quickly find areas that need improvement through correlation analysis and variable importance analysis, understand how much room noise increases when the NVH level of the system changes through sensitivity analysis, and reduce vehicle development time by improving efficiency. The method could be used in the development process and the validation of other deep learning and machine learning models. It could be an essential step in applying artificial intelligence, big data, and data analysis in the vehicle and mobility industry as a future vehicle development process