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Phycobilisomes (PBS) are antenna megacomplexes that transfer energy to photosystems II and I in thylakoids. PBS likely evolved from a basic, inefficient form into the predominant hemidiscoidal shape with radiating peripheral rods. However, it has been challenging to test this hypothesis because ancestral species are generally inaccessible. Here we use spectroscopy and cryo-electron microscopy to reveal a structure of a “paddle-shaped” PBS from a thylakoid-free cyanobacterium that likely retains ancestral traits. This PBS lacks rods and specialized ApcD and ApcF subunits, indicating relict characteristics. Other features include linkers connecting two chains of five phycocyanin hexamers (CpcN) and two core subdomains (ApcH), resulting in a paddle-shaped configuration. Energy transfer calculations demonstrate that chains are less efficient than rods. These features may nevertheless have increased light absorption by elongating PBS before multilayered thylakoids with hemidiscoidal PBS evolved. Our results provide insights into the evolution and diversification of light-harvesting strategies before the origin of thylakoids. 

Public opinion surveys constitute a widespread, powerful tool to study peoples’ attitudes and behaviors from comparative perspectives. However, even global surveys can have limited geographic and temporal coverage, which can hinder the production of comprehensive knowledge. To expand the scope of comparison, social scientists turn to ex-post harmonization of variables from datasets that cover similar topics but in different populations and/or at different times. These harmonized datasets can be analyzed as a single source and accessed through various data portals. However, the Survey Data Recycling (SDR) research project has identified three challenges faced by social scientists when using data portals: the lack of capability to explore data in-depth or query data based on customized needs, the difficulty in efficiently identifying related data for studies, and the incapability to evaluate theoretical models using sliced data. To address these issues, the SDR research project has developed the SDR Querier, which is applied to the harmonized SDR database. The SDR Querier includes a BERT-based model that allows for customized data queries through research questions or keywords (Query-by-Question), a visual design that helps users determine the availability of harmonized data for a given research question (Query-by-Condition), and the ability to reveal the underlying relational patterns among substantive and methodological variables in the database (Query-by-Relation), aiding in the rigorous evaluation or improvement of regression models. Case studies with multiple social scientists have demonstrated the usefulness and effectiveness of the SDR Querier in addressing daily challenges. 

Task‐incremental learning (Task‐IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under‐explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in‐depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time.

 

The labor-intensive nature of the construction industry requires workers to frequently perform physically demanding manual work, thereby exposing them to the risk of musculoskeletal injury (approximately 31.2 cases per 10,000 full-time equivalent workers). Exoskeletons and exosuits (collectively called EXOs here) are designed to protect workers from these injuries by reducing exertion and muscle fatigue during work. However, the usability of EXOs in construction is still not clear. This is because extant EXO assessments in construction were mainly conducted in laboratory environments with test participants who are not construction professionals. In this research, we conducted a pilot study to investigate the usability of EXOs in a real construction workplace. Four experienced workers were recruited to push/empty construction gondolas with and without a Back-Support EXO, HeroWear Apex. Three workers were recruited to install/remove wooden blocks between steel studs with and without two Arm-Support EXOs, i.e., Ekso EVO and Hilti EXO-001. Their motions, postures, heart rates, and task completion times were recorded and compared. The workers were also surveyed to gather their attitudes toward the EXO’s usefulness and ease of use. The study results demonstrated that the workers responded to the use of EXOs differently and consequently were not unanimously in favor of EXO adoption in practice. The preliminary results and findings from this pilot study help in building a foundation of understanding to improve EXO products to fit the needs of construction workers and foster EXO-enabled construction tasks in the future.