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Effects of Atmospheric Pollutants on Volatile‐Mediated Insect Ecosystem Services
No full textPrimary and secondary atmospheric pollutants, including carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOx), ozone (O3), sulphur dioxide (SO2) and particulate matter (PM2.5/PM10) with associated heavy metals (HMs) and micro- and nanoplastics (MPs/NPs), have the potential to influence and alter interspecific interactions involving insects that are responsible for providing essential ecosystem services (ESs). Given that insects rely on olfactory cues for vital processes such as locating mates, food sources and oviposition sites, volatile organic compounds (VOCs) are of paramount importance in interactions involving insects. While gaseous pollutants reduce the lifespan of individual compounds that act as olfactory cues, gaseous and particulate pollutants can alter their biosynthesis and emission and exert a direct effect on the olfactory system of insects. Consequently, air pollutants can affect ecosystem functioning and the services regulated by plant–insect interactions. This review examines the already identified and potential impacts of air pollutants on different aspects of VOC-mediated plant–insect interactions underlying a range of insect ES. Furthermore, we investigate the potential susceptibility of insects to future environmental changes and the adaptive mechanisms they may employ to efficiently detect odours. The current body of knowledge on the effects of air pollutants on key interspecific interactions is biased towards and limited to a few pollinators, herbivores and parasitoids on model plants. There is a notable absence of research on decomposers and seed dispersers. With exception of O3 and NOx, the effects of some widespread and emerging environmental pollutants, such as secondary organic aerosols (SOAs), SO2, HMs, PM and MPs/NPs, remain largely unexplored. It is recommended that the identified knowledge gaps be addressed in future research, with the aim of designing effective mitigation strategies for the adverse effects in question and developing robust conservation frameworks
(Part 1) Discovery of synthetic small molecules targeting the central regulator of Salmonella pathogenicity
No full text1435- HILD_noDNA_oleate_dimerA - 400 ns/day1436- HILD_noDNA_oleate_dimerB - 370 ns/day1437- HILD_noDNA_oleate_dimerC - 390 ns/day1438- HILD noDNA palmeate DimerB w2xZn2 13A3 (orthorombic) OPLS4 Maestro2024.2, 87k atoms1439- HILD noDNA APOSTRUCTURE DimerB w2xZn2 13A3 (orthorombic) OPLS4 Maestro2024.2, 87k atom
Puzzle-solving trace log data in a pair-programming digital educational escape room
No full textThis dataset accompanies the study titled "A Learning Analytics Perspective on Educational Escape Rooms," published inInteractive Learning Environments (https://doi.org/10.1080/10494820.2022.2041045). The study utilizes learning analytics to analyze participant interactions, behavioral sequences, and learning outcomes in a pair-programming educational escape room. The dataset comprises three files: File Descriptions: long_data.csvThis file contains event-level log data in SPELL format for each team during the escape room activity. Each row represents a single event and includes the following variables: teamId: Identifier for the team. from: Start time of the event (in minutes since the escape room's start). to: End time of the event (in minutes since the escape room's start). event: The type of event, which may include: Start Puzzle Solving 1-4 Hint Obtained Hint Failed to Obtain For more details about the meaning of these events, you can check the paper "A Learning Analytics Perspective on Educational Escape Rooms", or "Examining the Use of an Educational Escape Room for Teaching Programming in a Higher Education Setting." seq_all.csvThis file contains team-level sequence data in STS format, created with the seqformat function of TraMineR. Each row corresponds to a team. Each column represents a whole minute of the escape room (from minute 1 to 106, equivalent to 1 hour and 45 minutes). The values in the cells reflect what the team members were doing (event) field from the previous file. grades.csvThis file includes pre-test and post-test scores for each team of the two members. Each row represents a team and contains the following variables: teamId: Identifier for the team. Pre_1 and Pre_2: Pre-test scores for each of the two team members. Post_1 and Post_2: Post-test scores for each of the two team members.These scores capture individual learning outcomes before and after the escape room activity. Usage:This dataset is intended for researchers studying game-based learning, problem-solving processes, and the application of learning analytics in gamified educational settings. It supports the investigation of behavioral patterns, and learning outcomes in educational escape rooms. Licensing and Citation:When using this dataset, please cite the associated study (https://doi.org/10.1080/10494820.2022.2041045) and this Zenodo record. Contact Information:For further inquiries or information, please contact the corresponding author of the associated study
