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Abstract The R2R3-MYB transcription factor FveMYB10 is a major regulator of anthocyanin pigmentation in the red strawberry fruits. fvemyb10 loss-of-function mutants form yellow fruits but still accumulate purple-colored anthocyanins in the petioles, suggesting that anthocyanin biosynthesis is under distinct regulation in fruits and petioles. We identified a green petioles (gp)-1 mutant from chemical mutagenesis in the diploid wild strawberry Fragaria vesca that lacks anthocyanins in petioles. Using mapping-by-sequencing and transient functional assays, we confirmed that the causative mutation resides in a FveMYB10-Like (MYB10L) gene and that FveMYB10 and FveMYB10L function independently in the fruit and petiole respectively. In addition to their tissue-specific regulation, FveMYB10 and FveMYB10L respond differently to changes in light quality, produce distinct anthocyanin compositions, and preferentially activate different downstream anthocyanin biosynthesis genes in their respective tissues. This work identifies a new regulator of anthocyanin synthesis and demonstrates that two paralogous MYB genes with specialized functions enable tissue-specific regulation of anthocyanin biosynthesis in fruit and petiole tissues. 

We aim to develop methods for understanding how multimedia news exposure can affect people’s emotional responses, and we especially focus on news content related to gun violence, a very important yet polarizing issue in the U.S. We created the dataset NEmo+ by significantly extending the U.S. gun violence news-to-emotions dataset, BU-NEmo, from 320 to 1,297 news headline and lead image pairings and collecting 38,910 annotations in a large crowdsourcing experiment. In curating the NEmo+ dataset, we developed methods to identify news items that will trigger similar versus divergent emotional responses. For news items that trigger similar emotional responses, we compiled them into the NEmo+-Consensus dataset. We benchmark models on this dataset that predict a person’s dominant emotional response toward the target news item (single-label prediction). On the full NEmo+ dataset, containing news items that would lead to both differing and similar emotional responses, we also benchmark models for the novel task of predicting the distribution of evoked emotional responses in humans when presented with multi-modal news content. Our single-label and multi-label prediction models outperform baselines by large margins across several metrics. 

Focusing on a polarized issue—U.S. gun violence—this study examines agenda setting as an antecedent of political expression on social media. A state-of-the-art machine-learning model was used to analyze news coverage from 25 media outlets—mainstream and partisan. Those results were paired with a two-wave panel survey conducted during the 2018 U.S. midterm elections. Findings show mainstream media shape public opinion about gun violence, which then stimulates expression about the issue on social media. The study also reveals that partisan media’s gun violence coverage has significant cross-cutting effects. Notably, exposure to conservative media will decrease public salience of gun violence, pivot opinion in a more conservative direction, and discourage social media expression; and all of these effects are stronger among liberals.

 

Nonoverlapping sequential pattern mining is an important type of sequential pattern mining (SPM) with gap constraints, which not only can reveal interesting patterns to users but also can effectively reduce the search space using the Apriori (anti-monotonicity) property. However, the existing algorithms do not focus on attributes of interest to users, meaning that existing methods may discover many frequent patterns that are redundant. To solve this problem, this article proposes a task called nonoverlapping three-way sequential pattern (NTP) mining, where attributes are categorized according to three levels of interest: strong, medium, and weak interest. NTP mining can effectively avoid mining redundant patterns since the NTPs are composed of strong and medium interest items. Moreover, NTPs can avoid serious deviations (the occurrence is significantly different from its pattern) since gap constraints cannot match with strong interest patterns. To mine NTPs, an effective algorithm is put forward, called NTP-Miner, which applies two main steps: support (frequency occurrence) calculation and candidate pattern generation. To calculate the support of an NTP, depth-first and backtracking strategies are adopted, which do not require creating a whole Nettree structure, meaning that many redundant nodes and parent–child relationships do not need to be created. Hence, time and space efficiency is improved. To generate candidate patterns while reducing their number, NTP-Miner employs a pattern join strategy and only mines patterns of strong and medium interest. Experimental results on stock market and protein datasets show that NTP-Miner not only is more efficient than other competitive approaches but can also help users find more valuable patterns. More importantly, NTP mining has achieved better performance than other competitive methods in clustering tasks. Algorithms and data are available at: https://github.com/wuc567/Pattern-Mining/tree/master/NTP-Miner . 

Media framing refers to highlighting certain aspect of an issue in the news to promote a particular interpretation to the audience. Supervised learning has often been used to recognize frames in news articles, requiring a known pool of frames for a particular issue, which must be identified by communication researchers through thorough manual content analysis. In this work, we devise an unsupervised learning approach to discover the frames in news articles automatically. Given a set of news articles for a given issue, e.g., gun violence, our method first extracts frame elements from these articles using related Wikipedia articles and the Wikipedia category system. It then uses a community detection approach to identify frames from these frame elements. We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists. 

The dominance of flowering plants on earth is owed largely to the evolution of maternal tissues such as fruit and seedcoat that protect and disseminate the seeds. The mechanism of how fertilization triggers the development of these specialized maternal tissues is not well understood. A key event is the induction of auxin synthesis in the endosperm, and the mobile auxin subsequently stimulates seedcoat and fruit development. However, the regulatory mechanism of auxin synthesis in the endosperm remains unknown. Here, we show that a type I MADS box geneAGL62is required for the activation of auxin synthesis in the endosperm in bothFragaria vesca, a diploid strawberry, and in Arabidopsis. Several strawberryFveATHBgenes were identified as downstream targets ofFveAGL62and act to repress auxin biosynthesis. In this work, we identify a key mechanism for auxin induction to mediate fertilization success, a finding broadly relevant to flowering plants.

 

Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.