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Rapid progress in algal biotechnology has triggered a growing interest in hydrogel-encapsulated microalgal cultivation, especially for the engineering of functional photosynthetic materials and biomass production. An overlooked characteristic of gel-encapsulated cultures is the emergence of cell aggregates, which are the result of the mechanical confinement of the cells. Such aggregates have a dramatic effect on the light management of gel-encapsulated photobioreactors and hence strongly affect the photosynthetic outcome. To evaluate such an effect, we experimentally studied the optical response of hydrogels containing algal aggregates and developed optical simulations to study the resultant light intensity profiles. The simulations are validated experimentally via transmittance measurements using an integrating sphere and aggregate volume analysis with confocal microscopy. Specifically, the heterogeneous distribution of cell aggregates in a hydrogel matrix can increase light penetration while alleviating photoinhibition more effectively than in a flat biofilm. Finally, we demonstrate that light harvesting efficiency can be further enhanced with the introduction of scattering particles within the hydrogel matrix, leading to a fourfold increase in biomass growth. Our study, therefore, highlights a strategy for the design of spatially efficient photosynthetic living materials that have important implications for the engineering of future algal cultivation systems. 

Abstract Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families. 

When journalists cover a news story, they can cover the story from multiple angles or perspectives. These perspectives are called “frames,” and usage of one frame or another may influence public perception and opinion of the issue at hand. We develop a web-based system for analyzing frames in multilingual text documents. We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research. Users can use the framework to analyze multilingual text data, starting from the exploration of frames in user’s corpora and through review of previous framing literature (step 1-3) to frame classification (step 4) and prediction (step 5). The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art (SoTA) multilingual language model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Through the interactive website, anyone can perform the proposed computational framing analysis, making advanced computational analysis available to researchers without a programming background and bridging the digital divide within the communication research discipline in particular and the academic community in general. The system is available online at http://www.openframing.org, via an API http://www.openframing.org:5000/docs/, or through our GitHub page https://github.com/vibss2397/openFraming.