How many people need to classify the same image? A method for optimizing volunteer contributions in binary geographical classifications

Involving members of the public in image classification tasks that can be tricky to automate is increasingly recognized as a way to complete large amounts of these tasks and promote citizen involvement in science. While this labor is usually provided for free, it is still limited, making it important for researchers to use volunteer contributions as efficiently as possible. Using volunteer labor efficiently becomes complicated when individual tasks are assigned to multiple volunteers to increase confidence that the correct classification has been reached. In this paper, we develop a system to decide when enough information has been accumulated to confidently declare an image to be classified and remove it from circulation. We use a Bayesian approach to estimate the posterior distribution of the mean rating in a binary image classification task. Tasks are removed from circulation when user-defined certainty thresholds are reached. We demonstrate this process using a set of over 4.5 million unique classifications by 2783 volunteers of over 190,000 images assessed for the presence/absence of cropland. If the system outlined here had been implemented in the original data collection campaign, it would have eliminated the need for 59.4% of volunteer ratings. Had this effort been applied to new tasks, it would have allowed an estimated 2.46 times as many images to have been classified with the same amount of labor, demonstrating the power of this method to make more efficient use of limited volunteer contributions. To simplify implementation of this method by other investigators, we provide cutoff value combinations for one set of confidence levels

Austrian Research and Technology Report 2023

Der Forschungs- und Technologiebericht ist der Lagebericht über die aus Bundesmitteln geförderte Forschung, Technologie und Innovation in Österreich und wird im Auftrag des Bundesministeriums für Bildung, Wissenschaft und Forschung (BMBWF) in Einvernehmen mit dem Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie (BMK) sowie dem Bundesministerium für Arbeit und Wirtschaft (BMAW) erstellt. Der vorliegende Bericht steht im Zeichen eines komplexen Wandels auf unterschiedlichen Ebenen, einerseits getrieben durch multiple Krisen, die nicht nur das Innovationsverhalten von Unternehmen und wissenschaftlichen Akteurinnen und Akteuren verändern, sondern auch veränderte Rahmenbedingungen mit sich bringen. Die Twin Transition ist allgegenwärtig. Im vorliegenden Bericht wird mit dem Schwerpunktthema der Fokus auf die Grüne Transformation in Forschung und Wirtschaft gelegt. Abstrac

Österreichischer Forschungs- und Technologiebericht 2023

Der Forschungs- und Technologiebericht ist der Lagebericht über die aus Bundesmitteln geförderte Forschung, Technologie und Innovation in Österreich und wird im Auftrag des Bundesministeriums für Bildung, Wissenschaft und Forschung (BMBWF) in Einvernehmen mit dem Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie (BMK) sowie dem Bundesministerium für Arbeit und Wirtschaft (BMAW) erstellt. Der vorliegende Bericht steht im Zeichen eines komplexen Wandels auf unterschiedlichen Ebenen, einerseits getrieben durch multiple Krisen, die nicht nur das Innovationsverhalten von Unternehmen und wissenschaftlichen Akteurinnen und Akteuren verändern, sondern auch veränderte Rahmenbedingungen mit sich bringen. Die Twin Transition ist allgegenwärtig. Im vorliegenden Bericht wird mit dem Schwerpunktthema der Fokus auf die Grüne Transformation in Forschung und Wirtschaft gelegt

Exploring the Switchgrass Transcriptome Using Second-Generation Sequencing Technology

Background: Switchgrass (Panicum virgatum L.) is a C4 perennial grass and widely popular as an important bioenergy crop. To accelerate the pace of developing high yielding switchgrass cultivars adapted to diverse environmental niches, the generation of genomic resources for this plant is necessary. The large genome size and polyploid nature of switchgrass makes whole genome sequencing a daunting task even with current technologies. Exploring the transcriptional landscape using next generation sequencing technologies provides a viable alternative to whole genome sequencing in switchgrass. Principal Findings: Switchgrass cDNA libraries from germinating seedlings, emerging tillers, flowers, and dormant seeds were sequenced using Roche 454 GS-FLX Titanium technology, generating 980,000 reads with an average read length of 367 bp. De novo assembly generated 243,600 contigs with an average length of 535 bp. Using the foxtail millet genome as a reference greatly improved the assembly and annotation of switchgrass ESTs. Comparative analysis of the 454-derived switchgrass EST reads with other sequenced monocots including Brachypodium, sorghum, rice and maize indicated a 70– 80 % overlap. RPKM analysis demonstrated unique transcriptional signatures of the four tissues analyzed in this study. More than 24,000 ESTs were identified in the dormant seed library. In silico analysis indicated that there are more than 2000 EST-SSRs in this collection. Expression of several orphan ESTs was confirmed by RT-PCR. Significance: We estimate that about 90 % of the switchgrass gene space has been covered in this analysis. This study nearl

On-demand erythrocyte disposal and iron recycling requires transient macrophages in the liver

Iron is an essential component of the erythrocyte protein hemoglobin and is crucial to oxygen transport in vertebrates. In the steady state, erythrocyte production is in equilibrium with erythrocyte removal1. In various pathophysiological conditions, however, erythrocyte life span is severely compromised, which threatens the organism with anemia and iron toxicity2,3. Here we identify an on-demand mechanism that clears erythrocytes and recycles iron. We show that Ly-6Chigh monocytes ingest stressed and senescent erythrocytes, accumulate in the liver via coordinated chemotactic cues, and differentiate to ferroportin 1 (FPN1)-expressing macrophages that can deliver iron to hepatocytes. Monocyte-derived FPN1+ Tim-4neg macrophages are transient, reside alongside embryonically-derived Tim-4high Kupffer cells, and depend on Csf1 and Nrf2. The spleen likewise recruits iron-loaded Ly-6Chigh monocytes, but these do not differentiate into iron-recycling macrophages due to the suppressive action of Csf2. Inhibiting monocyte recruitment to the liver leads to kidney and liver damage. These observations identify the liver as the primary organ supporting rapid erythrocyte removal and iron recycling and uncover a mechanism by which the body adapts to fluctuations in erythrocyte integrity

Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task

The idea that closer things are more related than distant things, known as ‘Tobler’s first law of geography’, is fundamental to understanding many spatial processes. If this concept applies to volunteered geographic information (VGI), it could help to efficiently allocate tasks in citizen science campaigns and help to improve the overall quality of collected data. In this paper, we use classifications of satellite imagery by volunteers from around the world to test whether local familiarity with landscapes helps their performance. Our results show that volunteers identify cropland slightly better within their home country, and do slightly worse as a function of linear distance between their home and the location represented in an image. Volunteers with a professional background in remote sensing or land cover did no better than the general population at this task, but they did not show the decline with distance that was seen among other participants. Even in a landscape where pasture is easily confused for cropland, regional residents demonstrated no advantage. Where we did find evidence for local knowledge aiding classification performance, the realized impact of this effect was tiny. Rather, the inherent difficulty of a task is a much more important predictor of volunteer performance. These findings suggest that, at least for simple tasks, the geographical origin of VGI volunteers has little impact on their ability to complete image classifications