Structural integrity of the PCI domain of eIF3a TIF32 is required for mRNA recruitment to the 43S pre initiation complexes

Transfer of genetic information from genes into proteins is mediated by messenger RNA (mRNA) that must be first recruited to ribosomal pre-initiation complexes (PICs) by a mechanism that is still poorly understood. Recent studies showed that besides eIF4F and poly(A)-binding protein, eIF3 also plays a critical role in this process, yet the molecular mechanism of its action is unknown. We showed previously that the PCI domain of the eIF3c/NIP1 subunit of yeast eIF3 is involved in RNA binding. To assess the role of the second PCI domain of eIF3 present in eIF3a/TIF32, we performed its mutational analysis and identified a 10-Ala-substitution (Box37) that severely reduces amounts of model mRNA in the 43–48S PICs in vivo as the major, if not the only, detectable defect. Crystal structure analysis of the a/TIF32-PCI domain at 2.65-Å resolution showed that it is required for integrity of the eIF3 core and, similarly to the c/NIP1-PCI, is capable of RNA binding. The putative RNA-binding surface defined by positively charged areas contains two Box37 residues, R363 and K364. Their substitutions with alanines severely impair the mRNA recruitment step in vivo suggesting that a/TIF32-PCI represents one of the key domains ensuring stable and efficient mRNA delivery to the PICs

Case_Study_3_soil_plant_interaction_NIR

Raw NIR images with gps log and ground truth data for case study 3 soil and plants interaction. The RGB and NIR images were split as two files due to the file size limit

Case_Study_3_soil_plant_interaction_RGB

Raw RGB images with gps log and ground truth data for case study 3 soil and plants interaction. The RGB and NIR images were split as two files due to the file size limit

Senecio nikoensis Miq.

原著和名: サハギク科名: キク科 = Compositae採集地: 東京都 高尾山 (武蔵 高尾山)採集日: 1986/7/15採集者: 萩庭丈壽整理番号: JH007357国立科学博物館整理番号: TNS-VS-95735

Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs

Case_Study_1_plant_height_corn_Raw_Image_Data_3_of_10

Case_Study_1_plant_height_corn_Raw_Image_Data_3_of_1

Case_Study_1_plant_height_corn_Raw_Image_Data_5_of_10

Case_Study_1_plant_height_corn_Raw_Image_Data_5_of_1

Case_Study_1_plant_height_corn_Raw_Image_Data_8_of_10

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