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1. Plastic breeding system response to day length in the California wildflower Mimulus douglasii

   https://doi.org/10.1002/ajb2.1063  

   Barnett, Laryssa L. ; Troth, Ashley ; Willis, John H. ( April 2018 , American Journal of Botany)
2. Complex interactions between day length and diurnal patterns of gene expression drive photoperiodic responses in a perennial C 4 grass

   https://doi.org/10.1111/pce.13546  

   Weng, Xiaoyu ; Lovell, John T. ; Schwartz, Scott L. ; Cheng, Changde ; Haque, Taslima ; Zhang, Li ; Razzaque, Samsad ; Juenger, Thomas E. ( April 2019 , Plant, Cell & Environment)

   Photoperiod is a key environmental cue affecting flowering and biomass traits in plants. Key components of the photoperiodic flowering pathway have been identified in many species, but surprisingly few studies have globally examined the diurnal rhythm of gene expression with changes in day length. Using a cost‐effective 3′‐Tag RNA sequencing strategy, we characterize 9,010 photoperiod responsive genes with strict statistical testing across a diurnal time series in the C₄perennial grass,Panicum hallii. We show that the vast majority of photoperiod responses are driven by complex interactions between day length and sampling periods. A fine‐scale contrast analysis at each sampling time revealed a detailed picture of the temporal reprogramming ofcis‐regulatory elements and biological processes under short‐ and long‐day conditions. Phase shift analysis reveals quantitative variation among genes with photoperiod‐dependent diurnal patterns. In addition, we identify three photoperiod enriched transcription factor families with key genes involved in photoperiod flowering regulatory networks. Finally, coexpression networks analysis ofGIGANTEAhomolog predicted 1,668 potential coincidence partners, including five well‐known GI‐interacting proteins. Our results not only provide a resource for understanding the mechanisms of photoperiod regulation in perennial grasses but also lay a foundation to increase biomass yield in biofuel crops.

    

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3. Disparities in access and association between access to critical facilities during day-to-day and disrupted access as a result of storm extreme weather events

   https://doi.org/10.1016/j.heliyon.2023.e18841  

   Patrascu, Flavia Ioana ; Mostafavi, Ali ; Vedlitz, Arnold ( August 2023 , Heliyon)

   This study examines the relationship between households' access to critical facilities day-to-day and during weather-related extreme events. Despite a robust understanding of both day-to-day access and access during disasters, the interplay between the two remains unclear. To bridge this knowledge gap, we propose a novel empirical approach, using a Texas statewide household survey (N = 810). The survey evaluates day-to-day and past events access, exploring the experiences of respondents during multiple recent disasters, rather than focusing on a specific hazard. Using correlation analysis, we examined various access-related factors such as day-to-day trip duration, alternative trip duration, and loss of access during past events. Additionally, we evaluated the association between access-related factors and sociodemographic characteristics such as income, ethnicity, and urban status. The results indicate: (1) daily trip duration to critical facilities is associated with disrupted access during storm events, and (2) disparities persist during both day-to-day times and during extreme events. These results bring new insights to the existing body of knowledge on day-to-day access and access during disasters. The findings provide scientifically grounded evidence to city managers and planners, emphasizing the need for equitable distribution of facilities to enhance access to essential facilities both in daily life and during extreme weather-related events. 

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4. Day-to-day variability of ionosphere electron density during solar minimum derived from FORMOSAT-7/COSMIC-2 measurements

   https://doi.org/10.3319/TAO.2021.08.01.01  

   Rajesh, Panthalingal Krishanunni ; Lin, Charles C. ; Lin, Jia-Ting ; Lin, Chi-Yen ; Yue, Jia ; Matsuo, Tomoko ; Chen, Shih-Ping ; Chen, Chia-Hung ( January 2021 , Terrestrial, Atmospheric and Oceanic Sciences)
5. On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data

   https://doi.org/10.1109/BigData55660.2022.10020390  

   Talusan, Jose Paolo ; Mukhopadhyay, Ayan ; Freudberg, Dan ; Dubey, Abhishek ( December 2022 , 2022 IEEE International Conference on Big Data (Big Data))

   The ability to accurately predict public transit ridership demand benefits passengers and transit agencies. Agencies will be able to reallocate buses to handle under or over-utilized bus routes, improving resource utilization, and passengers will be able to adjust and plan their schedules to avoid overcrowded buses and maintain a certain level of comfort. However, accurately predicting occupancy is a non-trivial task. Various reasons such as heterogeneity, evolving ridership patterns, exogenous events like weather, and other stochastic variables, make the task much more challenging. With the progress of big data, transit authorities now have access to real-time passenger occupancy information for their vehicles. The amount of data generated is staggering. While there is no shortage in data, it must still be cleaned, processed, augmented, and merged before any useful information can be generated. In this paper, we propose the use and fusion of data from multiple sources, cleaned, processed, and merged together, for use in training machine learning models to predict transit ridership. We use data that spans a 2-year period (2020-2022) incorporating transit, weather, traffic, and calendar data. The resulting data, which equates to 17 million observations, is used to train separate models for the trip and stop level prediction. We evaluate our approach on real-world transit data provided by the public transit agency of Nashville, TN. We demonstrate that the trip level model based on Xgboost and the stop level model based on LSTM outperform the baseline statistical model across the entire transit service day. 

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