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Responses to the 2018 and 2019 “One Big Discovery” Question: ASTRO Membership’s Opinions on the Most Important Research Question Facing Radiation Oncology…Where Are We Headed?
No abstract prepared.
Too Big to Fail: Miracle Drugs or False Hope?
No abstract prepared.
BLASTNet: A call for community-involved big data in combustion machine learning
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Bacterial microcompartments: tiny organelles with big potential
Organization of metabolic processes within the space of a cell is critical for the survival of many organisms. In bacteria, spatial organization is achieved via proteinaceous organelles called bacterial microcompartments, which encapsulate pathway enzymes, substrates, and co-factors to drive the safe and efficient metabolism of niche carbon sources. Microcompartments are self-assembled from shell proteins that encapsulate a core comprising various enzymes. Here, this review discusses how recent advances in understanding microcompartment structure and assembly have informed engineering efforts to repurpose compartments and compartment-based structures for non-native functions. These advances, both in understanding of the native structure and function of compartments, as well as in the engineering of new functions, will pave the way for the use of these structures in bacterial cell factories.
Small Cells with Big Photosynthetic Productivities: Biotechnological Potential of the Picochlorum Genus
The Picochlorum genus is a distinctive eukaryotic green-algal clade that is the focus of several current biotechnological studies. It is capable of extremely rapid growth rates and has exceptional tolerances to high salinity, intense light, and elevated temperatures. Importantly, it has robust stability and high-biomass productivities in outdoor field trials in seawater. These features have propelled Picochlorum into the spotlight as a promising model for both fundamental and biotechnological research. Recently, several genetic tools, including genome editing, were developed for these algae, enabling insights into Picochlorum photophysiology and algal transformations for expanded capabilities. Here, we survey the Picochlorum genus, its genetic toolbox, recently characterized transformants, and discuss the commercial potential of Picochlorum as a salt-water photoautotrophic biocatalyst.
Big Data Analysis and Technical Review of Regeneration for Carbon Capture Processes
Carbon capture remains an integral technology to mitigate pollution from one of the most prevalent greenhouse gases. CO 2 desorption/absorbent regeneration for both solid- and liquid-based systems is widely recognized as an energy-intensive and costly process operation. Consequently, tremendous work was devoted towards developing new absorbents and regeneration processes to promote their economic feasibility for extensive implementation. In this review, we broadly and deeply review more than 10,000 papers and extract the hidden trends of carbon capture and absorbents regeneration in the past few decades, using a novel data-mining analysis technique. We comprehensively analyzed an array of recent absorbent regeneration methods utilized in post-combustion, pre-combustion, carbon capture from industrial point sources, and direct air carbon capture, with an emphasis on sorbent and solvent-based techniques. In conclusion, advanced regeneration methods in these techniques were illustrated and discussed, followed by recommendations for further research efforts.
Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis
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The Central Amazon Biomass Sink Under Current and Future Atmospheric CO 2 : Predictions From Big‐Leaf and Demographic Vegetation Models
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From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
Abstract The behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibrated model can reasonably propagate information from observations to unobserved variables via model physics, but traditional calibration is highly inefficient and results in non-unique solutions. Here we propose a novel differentiable parameter learning (dPL) framework that efficiently learns a global mapping between inputs (and optionally responses) and parameters. Crucially, dPL exhibits beneficial scaling curves not previously demonstrated to geoscientists: as training data increases, dPL achieves better performance, more physical coherence, and better generalizability (across space and uncalibrated variables), all with orders-of-magnitude lower computational cost. We demonstrate examples that learned from soil moisture and streamflow, where dPL drastically outperformed existing evolutionary and regionalization methods, or required only ~12.5% of the training data to achieve similar performance. The generic scheme promotes the integration of deep learning and process-based models, without mandating reimplementation.
Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility
Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10 8 potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.
Enabling pan-repository reanalysis for big data science of public metabolomics data
Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.
Automated classification of big X-ray diffraction data using deep learning models
Abstract In current in situ X-ray diffraction (XRD) techniques, data generation surpasses human analytical capabilities, potentially leading to the loss of insights. Automated techniques require human intervention, and lack the performance and adaptability required for material exploration. Given the critical need for high-throughput automated XRD pattern analysis, we present a generalized deep learning model to classify a diverse set of materials’ crystal systems and space groups. In our approach, we generate training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal properties. We also employ an expedited learning technique to refine our model’s expertise to experimental conditions. In addition, we optimize model architecture to elicit classification based on Bragg’s Law and use evaluation data to interpret our model’s decision-making. We evaluate our models using experimental data, materials unseen in training, and altered cubic crystals, where we observe state-of-the-art performance and even greater advances in space group classification.
Small partners, big impacts
Bioactive metabolites mediate interactions between hosts and their microbiomes, often to improve the fitness of the holobionts. However, the mechanistic basis of these interactions and their contributions to fitness are poorly understood owing to their multifaceted nature. Finally, recent studies integrating metabolomics with genomic and functional data are beginning to fill this gap.
Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures
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Surprisingly big linker-dependence of activity and selectivity in CO 2 reduction by an iridium(I) pincer complex
In this work, we report the quantitative electroreduction of CO 2 to CO by a PNP-pincer iridium(I) complex bearing amino linkers in DMF/water. The activity and selectivity of the reduction greatly depend on the choice of linker within the ligand. The complex 3-N is far superior to the analogues with methylene and oxygen linkers, showing higher activity and better selectivity for CO 2 over proton reduction.
Nanopores: synergy from DNA sequencing to industrial filtration – small holes with big impact
Nanopores in thin membranes play important roles in science and industry.