Comparison of Factors Affecting Crash Severities in Hit-and-Run and Non-Hit-and-Run Crashes

A hit-and-run (HR) crash occurs when the driver of the offending vehicle flees the crash scene without reporting it or aiding the victims. The current study aimed at contributing to existing literatures by comparing factors which might affect the crash severity in HR and non-hit-and-run (NHR) crashes. The data was extracted from the police-reported crash data from September 2017 to August 2018 within the City of Chicago. Two multinomial logistic regression models were established for the HR and NHR crash data, respectively. The odds ratio (OR) of each variable was used to quantify the impact of this variable on the crash severity. In both models, the property damage only (PDO) crash was selected as the reference group, and the injury and fatal crash were chosen as the comparison group. When the injury crash was taken as the comparison group, it was found that 12 variables contributed to the crash severities in both HR and NHR model. The average percentage deviation of OR for these 12 variables was 34%, indicating that compared with property damage, HR crashes were 34% more likely to result in injuries than NHR crashes on average. When fatal crashes were chosen as the comparison group, 2 variables were found to be statistically significant in both the HR and the NHR model. The average percentage deviation of OR for these 2 variables was 127%, indicating that compared with property damage, HR crashes were 127% more likely to result in fatalities than NHR crashes on average. Document type: Articl

Transcriptional profiling of Petunia seedlings reveals candidate regulators of the cold stress response

Petunias are important ornamentals with the capacity for cold acclimation. So far, there is limited information concerning gene regulation and signaling pathways related to the cold stress response in petunias. A custom-designed petunia microarray representing 24816 genes was used to perform transcriptome profiling in petunia seedlings subjected to cold at 2°C for 0.5 h, 2 h, 24 h and 5 d. A total of 2071 transcripts displayed differential expression patterns under cold stress, of which 1149 were up-regulated and 922 were down-regulated. Gene ontology enrichment analysis demarcated related biological processes, suggesting a possible link between flavonoid metabolism and plant adaptation to low temperatures. Many novel stress-responsive regulators were revealed, suggesting that diverse regulatory pathways may exist in petunias in addition to the well-characterized CBF pathway. The expression changes of selected genes under cold and other abiotic stress conditions were confirmed by real-time RT-PCR. Furthermore, weighted gene co-expression network analysis divided the petunia genes on the array into 65 modules that showed high co-expression and identified stress-specific hub genes with high connectivity. Our identification of these transcriptional responses and groups of differentially expressed regulators will facilitate the functional dissection of the molecular mechanism in petunias responding to environment stresses and extend our ability to improve cold tolerance in plants

What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerging in Online Social Movements through the Latent Dirichlet Allocation Model

Minority groups have been using social media to organize social movements that create profound social impacts. Black Lives Matter (BLM) and Stop Asian Hate (SAH) are two successful social movements that have spread on Twitter that promote protests and activities against racism and increase the public's awareness of other social challenges that minority groups face. However, previous studies have mostly conducted qualitative analyses of tweets or interviews with users, which may not comprehensively and validly represent all tweets. Very few studies have explored the Twitter topics within BLM and SAH dialogs in a rigorous, quantified and data-centered approach. Therefore, in this research, we adopted a mixed-methods approach to comprehensively analyze BLM and SAH Twitter topics. We implemented (1) the latent Dirichlet allocation model to understand the top high-level words and topics and (2) open-coding analysis to identify specific themes across the tweets. We collected more than one million tweets with the #blacklivesmatter and #stopasianhate hashtags and compared their topics. Our findings revealed that the tweets discussed a variety of influential topics in depth, and social justice, social movements, and emotional sentiments were common topics in both movements, though with unique subtopics for each movement. Our study contributes to the topic analysis of social movements on social media platforms in particular and the literature on the interplay of AI, ethics, and society in general.Comment: Accepted at AAAI and ACM Conference on AI, Ethics, and Society, August 1 to 3, 2022, Oxford, United Kingdo

METS-Based Cataloging Toolkit for Digital Library Management System

This toolkit is designed for the Digital Library Management System of Tsinghua University (TH-DLMS). The aim of TH-DLMS is to build up a platform to preserve various kinds of digitalized resources, manage distributed repositories and provide kinds of service for research and education. This toolkit fulfills the cataloging and preservation functions of TH-DLMS. METS (Metadata Encoding and T ransmission Standard) encoded documents are used as the final storage format of metadata, including descriptive metadata, structural metadata and administrative metadata, and submitted to a management system based on Fedora (Flexible Extensible Digital Object and Repository Architecture)