The swaps index for consumer choice

We extend the swaps index of rationality, introduced by Apesteguia and Ballester (2015) for a finite set of alternatives, to the standard consumer choice setting with infinite commodity spaces. Applications include consumer demand from competitive budget sets and the state-space approach to choice under uncertainty. We are primarily interested in Apesteguia and Ballester's result that the swaps index recovers the decision-maker's true preference from choice data for a large class of boundedly rational behavioral models. We show that this result still holds in the consumer choice setting under a suitably defined monotonicity condition. This condition is satisfied for various models of interest but violated for others

2017: HiMCM International Mathematics Modeling Contest: Finalist Rating

The integration of drones for light shows, alongside modes of delivery, have seen soaring popularity with modernity: both literally and physically. With the employment of drones in events like the Superbowl, and Intel light shows, drones’ uses have widespread implications that are quickly gaining momentum. In an order to implement these drones in light shows, adherence to regulations alongside quantifying and monitoring the path for a set number of drones is crucial. For an outdoor aerial light show that will display a Ferris wheel, a dragon, and another unique image, various factors need to be taken into account including the required airspace, launch site, safety considerations, and flight path. Our objective is to mathematically delineate the flight path for the drones and form the desired figures in the sky. We plan to achieve this goal by quantitatively analyzing the shape of the displays while calculating their path. By investigating these formations, we hope to have an efficient model for drone light shows that adhere to the standards and regulations

Is the Lone Scientist an American Dream? Perceived Communal Opportunities in STEM Offer a Pathway to Closing U.S.–Asia Gaps in Interest and Positivity

Abstract: The United States lags behind many Asian countries in engagement in science, technology, engineering, and mathematics (STEM). An unexplored factor in these country-level differences may be U.S.–Asia gaps in perceptions of the goal opportunities provided by STEM. Across four studies, U.S. students perceived fewer communal opportunities (working with/helping/relationships with others) in STEM than Asian students; this differential perception contributed to U.S.–Asia gaps in STEM interest. Perceptions of communal opportunities in STEM did not follow from a general orientation to perceive that all careers provided communal opportunities but from communal engagement in STEM. Perceptions about communal opportunities in STEM predicted STEM interest, and communal experience in STEM predicted STEM interest beyond quantity of STEM exposure. Experimentally highlighting the perceived communal opportunities in science closed the cultural gap in positivity toward a scientist career (Study 5). Perceptions of communal opportunities in STEM provide a new vantage point to improve U.S. engagement in STEM

Defining functional classes of Barth syndrome mutation in humans

The X-linked disease Barth syndrome (BTHS) is caused by mutations in TAZ; TAZ is the main determinant of the final acyl chain composition of the mitochondrial-specific phospholipid, cardiolipin. To date, a detailed characterization of endogenous TAZ has only been performed in yeast. Further, why a given BTHS-associated missense mutation impairs TAZ function has only been determined in a yeast model of this human disease. Presently, the detailed characterization of yeast tafazzin harboring individual BTHS mutations at evolutionarily conserved residues has identified seven distinct loss-of-function mechanisms caused by patient-associated missense alleles. However, whether the biochemical consequences associated with individual mutations also occur in the context of human TAZ in a validated mammalian model has not been demonstrated. Here, utilizing newly established monoclonal antibodies capable of detecting endogenous TAZ, we demonstrate that mammalian TAZ, like its yeast counterpart, is localized to the mitochondrion where it adopts an extremely protease-resistant fold, associates non-integrally with intermembrane space-facing membranes and assembles in a range of complexes. Even though multiple isoforms are expressed at the mRNA level, only a single polypeptide that co-migrates with the human isoform lacking exon 5 is expressed in human skin fibroblasts, HEK293 cells, and murine heart and liver mitochondria. Finally, using a new genome-edited mammalian BTHS cell culture model, we demonstrate that the loss-of-function mechanisms for two BTHS alleles that represent two of the seven functional classes of BTHS mutation as originally defined in yeast, are the same when modeled in human TAZ

Better Ate than Never: Reducing Wasted Food

Food waste in modern society is a problem which is quickly gaining traction. The Environmental Protection Agency (EPA) estimates that more food reaches landfills than any single material in the trash. This wastes food resources, while 42 million Americans are food-insecure and in need. In order to prevent food waste from reaching landfills and incinerators, we must scrutinize the sources of waste and take novel measures to make use of all edible food. In this paper, we examine three main problems using mathematical modeling. First, we created a model to see if food waste could be used to feed the food-insecure demographic of a state, using Texas as a test example. To model this we examined data about food waste percentage in North America and data about food production in Texas. We calculated food need based on the diets of children and adults, and computed the percentage of need that could be met using food waste. We found that between pessimistic and optimistic estimates, an average of 30% and 60% of food need can be met by food waste for demographics above and below the 185% poverty level. After comparing generated food waste to the needs of the food-insecure in Texas, we examined food consumption traits and habits to determine the food waste for specific household types. These included consumer units of both low and high income with varying family dynamics and ages. To address the problem we evaluated the percent of a household\u27s income spent on various types of food prepared at home, such as fruits, meat, and vegetables. Used data about the amount of food wasted at the consumer level by food type, we calculated the percent of a household\u27s income spent on wasted food by income bracket. We then adjusted this model to include out-of-home food waste and household size, creating a cohesive model for all households. Finally, we provided mathematical models of strategies for the repurposing of wasted food in Illinois. We found the value of specific food items and their rate of purchase from farms in Illinois over a one-year period. Our strategies redirected produce deemed physically unappealing to grocery stores, salvaging $2,396,640 of food that otherwise would have gone to waste. Other waste from the grocery stores is then brought to food shelters, or used in composting to provide both an economic and an environmental boon to the state

Content-Based Search for Deep Generative Models

The growing proliferation of customized and pretrained generative models has made it infeasible for a user to be fully cognizant of every model in existence. To address this need, we introduce the task of content-based model search: given a query and a large set of generative models, finding the models that best match the query. As each generative model produces a distribution of images, we formulate the search task as an optimization problem to select the model with the highest probability of generating similar content as the query. We introduce a formulation to approximate this probability given the query from different modalities, e.g., image, sketch, and text. Furthermore, we propose a contrastive learning framework for model retrieval, which learns to adapt features for various query modalities. We demonstrate that our method outperforms several baselines on Generative Model Zoo, a new benchmark we create for the model retrieval task.Comment: Our project page is hosted at https://generative-intelligence-lab.github.io/modelverse

Application of a broad range lytic phage LPST94 for biological control of salmonella in foods

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Salmonella, one of the most common food-borne pathogens, is a significant public health and economic burden worldwide. Lytic phages are viable alternatives to conventional technologies for pathogen biocontrol in food products. In this study, 40 Salmonella phages were isolated from environmentally sourced water samples. We characterized the lytic range against Salmonella and among all isolates, phage LPST94 showed the broadest lytic spectrum and the highest lytic activity. Electron microscopy and genome sequencing indicated that LPST94 belongs to the Ackermannviridae family. Further studies showed this phage is robust, tolerating a wide range of pH (4–12) and temperature (30–60◦C) over 60 min. The efficacy of phage LPST94 as a biological control agent was evaluated in various food products (milk, apple juice, chicken breast, and lettuce) inoculated with non-typhoidal Salmonella species at different temperatures. Interestingly, the anti-Salmonella efficacy of phage LPST94 was greater at 4◦C than 25◦C, although the efficacy varied between different food models. Adding phage LPST94 to Salmonella inoculated milk decreased the Salmonella count by 3 log10 CFU/mL at 4◦C and 0.84 to 2.56 log10 CFU/mL at 25◦C using an MOI of 1000 and 10000, respectively. In apple juice, chicken breast, and lettuce, the Salmonella count was decreased by 3 log10 CFU/mL at both 4◦C and 25◦C after applying phage LPST94 at an MOI of 1000 and 10,000, within a timescale of 48 h. The findings demonstrated that phage LPST94 is a promising candidate for biological control agents against pathogenic Salmonella and has the potential to be applied across different food matrices

Expression of Foxp3 in colorectal cancer but not in Treg cells correlates with disease progression in patients with colorectal cancer

Background: Regulatory T cells (Treg) expressing the transcription factor forkhead-box protein P3 (Foxp3) have been identified to counteract anti-tumor immune responses during tumor progression. Besides, Foxp3 presentation by cancer cells itself may also allow them to evade from effector T-cell responses, resulting in a survival benefit of the tumor. For colorectal cancer (CRC) the clinical relevance of Foxp3 has not been evaluated in detail. Therefore the aim of this study was to study its impact in colorectal cancer (CRC). Methods and Findings: Gene and protein analysis of tumor tissues from patients with CRC was performed to quantify the expression of Foxp3 in tumor infiltrating Treg and colon cancer cells. The results were correlated with clinicopathological parameters and patients overall survival. Serial morphological analysis demonstrated Foxp3 to be expressed in cancer cells. High Foxp3 expression of the cancer cells was associated with poor prognosis compared to patients with low Foxp3 expression. In contrast, low and high Foxp3 level in tumor infiltrating Treg cells demonstrated no significant differences in overall patient survival. Conclusions: Our findings strongly suggest that Foxp3 expression mediated by cancer cells rather than by Treg cells contribute to disease progression