The role of gossip in the evolution of cooperation

The prevalence of cooperation in human societies is astonishing. Scholars from many disciplines have been sought to understand why it evolves. Some studies have indicated that gossip may play an important role in the evolution of cooperation. However, there has yet to be a systematic attempt to test this hypothesis directly. In this thesis, I developed an evolutionary game theoretic model and examined the role of gossip in the evolution of cooperation as well as the mechanism of the evolution of gossipers. I found that gossip increases reputation accessibility and makes the utilization of reputation information effective and necessary. The utilization of reputation information not only leads to more cooperation but also motivates individuals to manage their reputation by cooperating more with gossipers. As a result, gossipers gain an advantage over non-gossipers, and this leads to the evolution of gossipers. I also examined the factors that moderate these results

Personalized Food Image Classification: Benchmark Datasets and New Baseline

Food image classification is a fundamental step of image-based dietary assessment, enabling automated nutrient analysis from food images. Many current methods employ deep neural networks to train on generic food image datasets that do not reflect the dynamism of real-life food consumption patterns, in which food images appear sequentially over time, reflecting the progression of what an individual consumes. Personalized food classification aims to address this problem by training a deep neural network using food images that reflect the consumption pattern of each individual. However, this problem is under-explored and there is a lack of benchmark datasets with individualized food consumption patterns due to the difficulty in data collection. In this work, we first introduce two benchmark personalized datasets including the Food101-Personal, which is created based on surveys of daily dietary patterns from participants in the real world, and the VFNPersonal, which is developed based on a dietary study. In addition, we propose a new framework for personalized food image classification by leveraging self-supervised learning and temporal image feature information. Our method is evaluated on both benchmark datasets and shows improved performance compared to existing works. The dataset has been made available at: https://skynet.ecn.purdue.edu/~pan161/dataset_personal.htmlComment: Accepted by IEEE Asilomar conference (2023

Muti-Stage Hierarchical Food Classification

Food image classification serves as a fundamental and critical step in image-based dietary assessment, facilitating nutrient intake analysis from captured food images. However, existing works in food classification predominantly focuses on predicting 'food types', which do not contain direct nutritional composition information. This limitation arises from the inherent discrepancies in nutrition databases, which are tasked with associating each 'food item' with its respective information. Therefore, in this work we aim to classify food items to align with nutrition database. To this end, we first introduce VFN-nutrient dataset by annotating each food image in VFN with a food item that includes nutritional composition information. Such annotation of food items, being more discriminative than food types, creates a hierarchical structure within the dataset. However, since the food item annotations are solely based on nutritional composition information, they do not always show visual relations with each other, which poses significant challenges when applying deep learning-based techniques for classification. To address this issue, we then propose a multi-stage hierarchical framework for food item classification by iteratively clustering and merging food items during the training process, which allows the deep model to extract image features that are discriminative across labels. Our method is evaluated on VFN-nutrient dataset and achieve promising results compared with existing work in terms of both food type and food item classification.Comment: accepted for ACM MM 2023 Madim

Designing a Virtual Reality Video for Disability Inclusion: An Action Design Research

Social inclusion of people with disability is one of the priorities of the United Nations Sustainable Development Goal 10 – Reduced Inequalities. However, people with disability still face exclusion in society, such as inaccessible facilities and negative social attitudes. In this research, we explore virtual reality (VR) as a tool to raise the general public’s awareness of disability. We conduct an action design research to develop an immersive VR video that promotes disability inclusion, in collaboration with disability practitioners, VR practitioners, disability researchers, and individuals using wheelchairs. In this Short Paper, we present three initial design principles that are critical to addressing three key challenges in disability awareness raising. Our VR artefact, once completed, will serve as a tool for disability awareness raising. The prescriptive knowledge developed could inspire Information Systems researchers and practitioners to explore a similar class of artefacts that promote social inclusion through cultivating awareness and behavioural changes

Leveraging IS-based Energy Systems for Energy Poverty Alleviation in Zambia: An Interpretive Case Study

Energy poverty is a pressing societal challenge, affecting over 700 million people worldwide, particularly underserved communities. Although information systems (IS) resources have been made available to alleviate energy poverty, realizing their effective use for intended impacts remains challenging. In this ongoing research, we adopt a resourcing perspective to explore how IS resources can be effectively used to alleviate energy poverty in underserved contexts. We present a community case study in Lusaka, Zambia, where the effective use of IS-based Energy Systems (IES) has yielded promising results. We develop an initial framework that explains “what it takes” to realize effective IES resourcing for energy poverty alleviation, including mechanisms (i.e., gap spotting, narratives, and scaffolding) and actors (i.e., resource providers and users) in three stages - resourcing IN, resourcing WITHIN, and resourcing OUT. We also indicate the next steps of this study and expected contributions and discuss implications for future research

Fork PCR: a universal and efficient genome-walking tool

The reported genome-walking methods still suffer from some deficiencies, such as cumbersome experimental steps, short target amplicon, or deep background. Here, a simple and practical fork PCR was proposed for genome-walking. The fork PCR employs a fork primer set of three random oligomers to implement walking task. In primary fork PCR, the low-stringency amplification cycle mediates the random binding of primary fork primer to some places on genome, producing a batch of single-stranded DNAs. In the subsequent high-stringency amplification, the target single-strand is processed into double-strand by the site-specific primer, but a non-target single-stranded DNA cannot be processed by any primer. As a result, only the target DNA can be exponentially amplified in the remaining high-stringency cycles. Secondary/tertiary nested fork PCR(s) further magnifies the amplification difference between the both DNAs by selectively enriching target DNA. The applicability of fork PCR was validated by walking several gene loci. The fork PCR could be a perspective substitution for the existing genome-walking schemes

A body map of super-enhancers and their function in pig

IntroductionSuper-enhancers (SEs) are clusters of enhancers that act synergistically to drive the high-level expression of genes involved in cell identity and function. Although SEs have been extensively investigated in humans and mice, they have not been well characterized in pigs.MethodsHere, we identified 42,380 SEs in 14 pig tissues using chromatin immunoprecipitation sequencing, and statistics of its overall situation, studied the composition and characteristics of SE, and explored the influence of SEs characteristics on gene expression.ResultsWe observed that approximately 40% of normal enhancers (NEs) form SEs. Compared to NEs, we found that SEs were more likely to be enriched with an activated enhancer and show activated functions. Interestingly, SEs showed X chromosome depletion and short interspersed nuclear element enrichment, implying that SEs play an important role in sex traits and repeat evolution. Additionally, SE-associated genes exhibited higher expression levels and stronger conservation than NE-associated genes. However, genes with the largest SEs had higher expression levels than those with the smallest SEs, indicating that SE size may influence gene expression. Moreover, we observed a negative correlation between SE gene distance and gene expression, indicating that the proximity of SEs can affect gene activity. Gene ontology enrichment and motif analysis revealed that SEs have strong tissue-specific activity. For example, the CORO2B gene with a brain-specific SE shows strong brain-specific expression, and the phenylalanine hydroxylase gene with liver-specific SEs shows strong liver-specific expression.DiscussionIn this study, we illustrated a body map of SEs and explored their functions in pigs, providing information on the composition and tissue-specific patterns of SEs. This study can serve as a valuable resource of gene regulatory and comparative analyses to the scientific community and provides a theoretical reference for genetic control mechanisms of important traits in pigs