Role of C4 carbon fixation in Ulva prolifera, the macroalga responsible for the world's largest green tides

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Liu, D., Ma, Q., Valiela, I., Anderson, D. M., Keesing, J. K., Gao, K., Zhen, Y., Sun, X., & Wang, Y. Role of C4 carbon fixation in Ulva prolifera, the macroalga responsible for the world's largest green tides. Communications Biology, 3(1), (2020): 494, doi:10.1038/s42003-020-01225-4.Most marine algae preferentially assimilate CO2 via the Calvin-Benson Cycle (C3) and catalyze HCO3− dehydration via carbonic anhydrase (CA) as a CO2-compensatory mechanism, but certain species utilize the Hatch-Slack Cycle (C4) to enhance photosynthesis. The occurrence and importance of the C4 pathway remains uncertain, however. Here, we demonstrate that carbon fixation in Ulva prolifera, a species responsible for massive green tides, involves a combination of C3 and C4 pathways, and a CA-supported HCO3− mechanism. Analysis of CA and key C3 and C4 enzymes, and subsequent analysis of δ13C photosynthetic products showed that the species assimilates CO2 predominately via the C3 pathway, uses HCO3− via the CA mechanism at low CO2 levels, and takes advantage of high irradiance using the C4 pathway. This active and multi-faceted carbon acquisition strategy is advantageous for the formation of massive blooms, as thick floating mats are subject to intense surface irradiance and CO2 limitation.This work was supported by the State Key Project of Research and Development Plan, Ministry of Science and Technology of the Peopleʼs Republic of China (2016YFC1402106). Support for D.M.A. provided by the Woods Hole Oceanographic Institution—Ocean University of China Cooperative Research Initiative. We thank Dr. Juntian Xu, Jing Ma, Ying Li, and Chenglong Ji for assisting culture experiments and sample analysis

Scale Up Event Extraction Learning via Automatic Training Data Generation

The task of event extraction has long been investigated in a supervised learning paradigm, which is bound by the number and the quality of the training instances. Existing training data must be manually generated through a combination of expert domain knowledge and extensive human involvement. However, due to drastic efforts required in annotating text, the resultant datasets are usually small, which severally affects the quality of the learned model, making it hard to generalize. Our work develops an automatic approach for generating training data for event extraction. Our approach allows us to scale up event extraction training instances from thousands to hundreds of thousands, and it does this at a much lower cost than a manual approach. We achieve this by employing distant supervision to automatically create event annotations from unlabelled text using existing structured knowledge bases or tables.We then develop a neural network model with post inference to transfer the knowledge extracted from structured knowledge bases to automatically annotate typed events with corresponding arguments in text.We evaluate our approach by using the knowledge extracted from Freebase to label texts from Wikipedia articles. Experimental results show that our approach can generate a large number of highquality training instances. We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events

The IPIN 2019 Indoor Localisation Competition—Description and Results

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks

Use of nanomaterials in the pretreatment of water samples for environmental analysis

The challenge of providing clean drinking water is of enormous relevance in today’s human civilization, being essential for human consumption, but also for agriculture, livestock and several industrial applications. In addition to remediation strategies, the accurate monitoring of pollutants in water sup-plies, which most of the times are present at low concentrations, is a critical challenge. The usual low concentration of target analytes, the presence of in-terferents and the incompatibility of the sample matrix with instrumental techniques and detectors are the main reasons that renders sample preparation a relevant part of environmental monitoring strategies. The discovery and ap-plication of new nanomaterials allowed improvements on the pretreatment of water samples, with benefits in terms of speed, reliability and sensitivity in analysis. In this chapter, the use of nanomaterials in solid-phase extraction (SPE) protocols for water samples pretreatment for environmental monitoring is addressed. The most used nanomaterials, including metallic nanoparticles, metal organic frameworks, molecularly imprinted polymers, carbon-based nanomaterials, silica-based nanoparticles and nanocomposites are described, and their applications and advantages overviewed. Main gaps are identified and new directions on the field are suggested.publishe

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001