Materials with hierarchical porosity and structures have been heavily involved in newly developed energy storage and conversion systems. Because of meticulous design and ingenious hierarchical ...structuration of porosities through the mimicking of natural systems, hierarchically structured porous materials can provide large surface areas for reaction, interfacial transport, or dispersion of active sites at different length scales of pores and shorten diffusion paths or reduce diffusion effect. By the incorporation of macroporosity in materials, light harvesting can be enhanced, showing the importance of macrochannels in light related systems such as photocatalysis and photovoltaics. A state‐of‐the‐art review of the applications of hierarchically structured porous materials in energy conversion and storage is presented. Their involvement in energy conversion such as in photosynthesis, photocatalytic H2 production, photocatalysis, or in dye sensitized solar cells (DSSCs) and fuel cells (FCs) is discussed. Energy storage technologies such as Li‐ions batteries, supercapacitors, hydrogen storage, and solar thermal storage developed based on hierarchically porous materials are then discussed. The links between the hierarchically porous structures and their performances in energy conversion and storage presented can promote the design of the novel structures with advanced properties.
Hierarchically structured porous materials have been heavily involved in newly developed energy storage and conversion systems because of their high surface area, tunable chemical compositions, excellent mechanical and chemical resistance, controllable micro‐, meso‐, and macroporosities at different length scales, desired multifunctionalities, reduced diffusion effect, light harvesting properties, and confinement effects.
Prototype theory, which argues that categories have graded (and thus fuzzy) membership based on prototypes, has been used as cognitive evidence to support moral particularism because if categories ...(in moral rules) only have fuzzy conceptual boundaries, moral rules are not enough for moral judgment, as specific situations also need to be considered to determine how these fuzzy categories should be understood, which is what moral particularism believes. The importance of literature for ethics, especially for moral imagination, has also been extensively discussed because literature can provide vivid examples for us to imagine different moral dilemmas, the consequences of different moral choices, and the feelings of different people facing different situations. Martha Nussbaum specifically argues that the literary form is the only adequate form to imagine certain complex moral situations. By analyzing concrete literary examples as well as the related ethical discussions and empirical findings, this article argues that, building on Nussbaum's argument, prototype theory can serve as a cognitive basis for the importance of literary form for moral imagination, because the literary form's tolerance of ambiguity suits how we ambiguously categorize the world.
Modeling dynamic geophysical phenomena is at the core of Earth and environmental studies. The geoscientific community relying mainly on physical representations may want to consider much deeper ...adoption of artificial intelligence (AI) instruments in the context of AI's global success and emergence of big Earth data. A new perspective of using hybrid physics‐AI approaches is a grand vision, but actualizing such approaches remains an open question in geoscience. This study develops a general approach to improving AI geoscientific awareness, wherein physical approaches such as temporal dynamic geoscientific models are included as special recurrent neural layers in a deep learning architecture. The illustrative case of runoff modeling across the conterminous United States demonstrates that the physics‐aware DL model has enhanced prediction accuracy, robust transferability, and good intelligence for inferring unobserved processes. This study represents a firm step toward realizing the vision of tackling Earth system challenges by physics‐AI integration.
Plain Language Summary
Artificial intelligence (AI) learns and makes inferences from experience and resembles the way untaught humans learn. If scientists can manage to teach AI the physical rules of the world, the “educated” AI may be more intelligent in deductions. However, how to implant elements of physical representations into an AI system effectively and directly remains an open question. This study proposes a general framework and applicable solutions to this challenge in the context of Earth science. The novel framework has a specially structured design for an AI system to “memorize” physical rules behind system dynamics (i.e., how a geosystem evolves with time). Following this framework, we developed a hydrology‐aware deep learning model to simulate/predict runoff in 569 catchments across the conterminous United States. The results show that after “learning” a hydrologic model, the AI system has enhanced prediction accuracy and good intelligence to deal with unfamiliar regions and infer unobserved processes. The potential of AI for in‐depth information mining, in return, fills the knowledge gap existing in physical approaches. The symbiotic integration of physical approaches and deep learning represents a promising solution to improve AI system awareness of geoscience knowledge.
Key Points
A novel approach to representing geosystem dynamics via a recurrent neural network within deep learning architectures is proposed
The physics‐aware AI system exhibits robust transferability and good intelligence for inferring unobserved processes in runoff modeling
The hydrology‐aware DL model can be an intelligent parameterization module for the encoded hydrologic model in cross‐region applications
The adverse environmental impacts of the fossil fuel and the concerns of energy security necessitate the development of alternative clean energy sources from renewable feedstocks. Lignocellulosic ...biomass is a 2nd generation feedstock used in the production of biofuels and bio-based products that are conventionally derived from fossil resources. The biochemical conversion, which entails biomass pretreatment, enzymatic hydrolysis and fermentation, is one major platform used to transform lignocelluloses into biofuels. However, lignin presents many challenges to enzymatic hydrolysis leading to the need of high enzyme dose, low hydrolysis yield, low level of recyclability, high cost of enzymatic hydrolysis (because of the high cost of enzymes), and so on. Therefore, enzymatic hydrolysis, which is not cost effective, becomes one of major cost contributors. To mitigate the negative effects of lignin, extensive research has been conducted to explore the fundamental mechanisms of lignin-enzyme interactions to develop technologies to overcome the negative effects of lignin on enzymatic hydrolysis. Non-productive adsorption, which is characterized by hydrophobic, electrostatic and/or hydrogen bonding interactions, is widely known as the primary mechanism governing lignin-enzyme interactions. In addition, lignin-enzyme interaction is also influenced by steric hindrance (i.e., the physical blocking of enzyme access to carbohydrates by lignin). However, the mechanisms underlying the lignin-enzyme interactions remain unclear. This article aims to present a comprehensive review on the lignin-enzyme interactions (i.e. the mechanism, governing driving forces, modeling, and technologies for mitigating the negative effect of lignin). The current challenges inherent in this process and possible avenues of research in cellulosic biorefinery conclude this article.
Long short‐term memory (LSTM) networks represent one of the most prevalent deep learning (DL) architectures in current hydrological modeling, but they remain black boxes from which process ...understanding can hardly be obtained. This study aims to demonstrate the potential of interpretive DL in gaining scientific insights using flood prediction across the contiguous United States (CONUS) as a case study. Two interpretation methods were adopted to decipher the machine‐captured patterns and inner workings of LSTM networks. The DL interpretation by the expected gradients method revealed three distinct input‐output relationships learned by LSTM‐based runoff models in 160 individual catchments. These relationships correspond to three flood‐inducing mechanisms—snowmelt, recent rainfall, and historical rainfall—that account for 10.1%, 60.9%, and 29.0% of the 20,908 flow peaks identified from the data set, respectively. Single flooding mechanisms dominate 70.7% of the investigated catchments (11.9% snowmelt‐dominated, 34.4% recent rainfall‐dominated, and 24.4% historical rainfall‐dominated mechanisms), and the remaining 29.3% have mixed mechanisms. The spatial variability in the dominant mechanisms reflects the catchments' geographic and climatic conditions. Moreover, the additive decomposition method unveils how the LSTM network behaves differently in retaining and discarding information when emulating different types of floods. Information from inputs within previous time steps can be partially stored in the memory of LSTM networks to predict snowmelt‐induced and historical rainfall‐induced floods, while for recent rainfall‐induced floods, only recent information is retained. Overall, this study provides a new perspective for understanding hydrological processes and extremes and demonstrates the prospect of artificial intelligence‐assisted scientific discovery in the future.
Key Points
The expected gradients method was used to retrieve flooding mechanisms learned by long short‐term memory networks in 160 catchments in the United States
Snowmelt, recent rainfall, and historical rainfall induce 10.1%, 60.9%, and 29.0% of the 20,908 identified flow peaks, respectively
The additive decomposition method unveiled models' distinct behaviors in retaining and discarding information for different flood types
The ubiquitin‐proteasome system (UPS) is a complex process that regulates protein stability and activity by the sequential actions of E1, E2 and E3 enzymes to influence diverse aspects of eukaryotic ...cells. However, due to the diversity of proteins in cells, substrate selection is a highly critical part of the process. As a key player in UPS, E3 ubiquitin ligases recruit substrates for ubiquitination specifically. Among them, RING E3 ubiquitin ligases which are the most abundant E3 ubiquitin ligases contribute to diverse cellular processes. The multisubunit cullin‐RING ligases (CRLs) are the largest family of RING E3 ubiquitin ligases with tremendous plasticity in substrate specificity and regulate a vast array of cellular functions. The F‐box protein Skp2 is a component of CRL1 (the prototype of CRLs) which is expressed in many tissues and participates in multiple cellular functions such as cell proliferation, metabolism, and tumorigenesis by contributing to the ubiquitination and subsequent degradation of several specific tumor suppressors. Most importantly, Skp2 plays a pivotal role in a plethora of cancer‐associated signaling pathways. It enhances cell growth, accelerates cell cycle progression, promotes migration and invasion, and inhibits cell apoptosis among others. Hence, targeting Skp2 may represent a novel and attractive strategy for the treatment of different human cancers overexpressing this oncogene. In this review article, we summarized the known roles of Skp2 both in health and disease states in relation to the UPS.
SUMMARY
High yield and stress resistance are the major prerequisites for successful crop cultivation, and can be achieved by modifying plant architecture. Evolutionarily conserved growth‐regulating ...factors (GRFs) control the growth of different tissues and organs of plants. Here, we provide a systematic overview of the expression patterns of GRF genes and the structural features of GRF proteins in different plant species. Moreover, we illustrate the conserved and divergent roles of GRFs, microRNA396 (miR396), and GRF‐interacting factors (GIFs) in leaf, root, and flower development. We also describe the molecular networks involving the miR396–GRF–GIF module, and illustrate how this module coordinates with different signaling molecules and transcriptional regulators to control development of different plant species. GRFs promote leaf growth, accelerate grain filling, and increase grain size and weight. We also provide some molecular insight into how coordination between GRFs and other signaling modules enhances crop productivity; for instance, how the GRF–DELLA interaction confers yield‐enhancing dwarfism while increasing grain yield. Finally, we discuss how the GRF‐GIF chimera substantially improves plant transformation efficiency by accelerating shoot formation. Overall, we systematically review the conserved and divergent roles of GRFs and the miR396–GRF–GIF module in growth regulation, and also provide insights into how GRFs can be utilized to improve the productivity and nutrient content of crop plants.
Significance Statement
Growth‐regulating factors (GRFs) are evolutionarily conserved transcription factors that control the growth of tissues and organs in different plant species. Our work systematically reviews the conserved and divergent roles of GRFs and the miR396–GRF/GIF module in growth regulation and stress adaptation and also provides insights into how GRFs can be utilized to improve crop yield, stress resistance, and nutrient content.
Summary
Abscisic acid (ABA) plays a crucial role in the adaptation of young seedlings to environmental stresses. However, the role of epigenetic components and core transcriptional machineries in the ...effect of ABA on seed germination and seedling growth remain unclear.
Here, we show that a histone 3 lysine 4 (H3K4) demethylase, JMJ17, regulates the expression of ABA‐responsive genes during seed germination and seedling growth. Using comparative interactomics, WRKY40, a central transcriptional repressor in ABA signaling, was shown to interact with JMJ17. WRKY40 facilitates the recruitment of JMJ17 to the ABI5 chromatin, which removes gene activation marks (H3K4me3) from the ABI5 chromatin, thereby repressing its expression.
Additionally, WRKY40 represses the transcriptional activation activity of HY5, which can activate ABI5 expression by directly binding to its promoter. An increase in ABA concentrations decreases the affinity of WRKY40 for the ABI5 promoter. Thus, WRKY40 and JMJ17 are released from the ABI5 chromatin, activating HY5. The accumulated ABI5 protein further shows heteromeric interaction with HY5, and thus synergistically activates its own expression.
Our findings reveal a novel transcriptional switch, composed of JMJ17–WRKY40 and HY5–ABI5 modules, which regulates the ABA response during seed germination and seedling development in Arabidopsis.
Lignocellulosic biomass is an abundant organic material that can be used for sustainable production of bioenergy and biofuels such as biogas (about 50–75% CH4 and 25–50% CO2). Out of all ...bioconversion technologies for biofuel and bioenergy production, anaerobic digestion (AD) is a most cost-effective bioconversion technology that has been implemented worldwide for commercial production of electricity, heat, and compressed natural gas (CNG) from organic materials. However, the utilization of lignocellulosic biomass for biogas production via anaerobic digestion has not been widely adopted because the complicated structure of the plant cell wall makes it resistant to microbial attack. Pretreatment of recalcitrant lignocellulosic biomass is essential to achieve high biogas yield in the AD process. A number of different pretreatment techniques involving physical, chemical, and biological approaches have been investigated over the past few decades, but there is no report that systematically compares the performance of these pretreatment methods for application on lignocellulosic biomass for biogas production. This paper reviews the methods that have been studied for pretreatment of lignocellulosic biomass for conversion to biogas. It describes the AD process, structural and compositional properties of lignocellulosic biomass, and various pretreatment techniques, including the pretreatment process, parameters, performance, and advantages vs. drawbacks. This paper concludes with the current status and future research perspectives of pretreatment.
DNA methylation and histone modification are evolutionarily conserved epigenetic modifications that are crucial for the expression regulation of abiotic stress‐responsive genes in plants. Dynamic ...changes in gene expression levels can result from changes in DNA methylation and histone modifications. In the last two decades, how epigenetic machinery regulates abiotic stress responses in plants has been extensively studied. Here, based on recent publications, we review how DNA methylation and histone modifications impact gene expression regulation in response to abiotic stresses such as drought, abscisic acid, high salt, extreme temperature, nutrient deficiency or toxicity, and ultraviolet B exposure. We also review the roles of epigenetic mechanisms in the formation of transgenerational stress memory. We posit that a better understanding of the epigenetic underpinnings of abiotic stress responses in plants may facilitate the design of more stress‐resistant or ‐resilient crops, which is essential for coping with global warming and extreme environments.
DNA methylation and histone modification are evolutionarily conserved epigenetic modifications that are crucial for regulating the expression of abiotic stress‐responsive genes in plants. This review describes the epigenetic mechanisms of abiotic stress responses such as drought, high salt, extreme temperature and nutrient deficiency in plants.
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