Nitrogen (N), potassium (K), and phosphorus (P) are essential macronutrients for plant growth and development, and their availability affects crop yield. Compared with N, the relatively low ...availability of K and P in soils limits crop production and thus threatens food security and agricultural sustainability. Improvement of plant nutrient utilization efficiency provides a potential route to overcome the effects of K and P deficiencies. Investigation of the molecular mechanisms underlying how plants sense, absorb, transport, and use K and P is an important prerequisite to improve crop nutrient utilization efficiency. In this review, we summarize current understanding of K and P transport and signaling in plants, mainly taking Arabidopsis thaliana and rice (Oryza sativa) as examples. We also discuss the mechanisms coordinating transport of N and K, as well as P and N.
Potassium (K) and phosphorus (P) are essential macronutrients for plant growth, development, and crop yield. This review summarizes the current understanding of K and P transport and signaling in plants and discusses the mechanisms coordinating N (nitrogen), K, and P.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
In this paper, we propose two metrics, i.e., the optimal repair time and the resilience reduction worth, to measure the criticality of the components of a network system from the perspective of their ...contribution to system resilience. Specifically, the two metrics quantify: 1) the priority with which a failed component should be repaired and re-installed into the network and 2) the potential loss in the optimal system resilience due to a time delay in the recovery of a failed component, respectively. Given the stochastic nature of disruptive events on infrastructure networks, a Monte Carlo-based method is proposed to generate probability distributions of the two metrics for all of the components of the network; then, a stochastic ranking approach based on the Copeland's pairwise aggregation is used to rank components importance. Numerical results are obtained for the IEEE 30-bus test network and a comparison is made with three classical centrality measures.
Increased vascular permeability facilitates metastasis. Emerging evidence indicates that secreted microRNAs (miRNAs) may mediate the crosstalk between cancer and stromal cells. To date, whether and ...how secreted miRNAs affect vascular permeability remains unclear. Based on deep sequencing and quantitative PCR, we found that higher level of serum miR‐103 was associated with higher metastasis potential of hepatocellular carcinoma (HCC). The in vitro endothelial permeability and transendothelial invasion assays revealed that the conditioned media or exosomes derived from high miR‐103‐expressing hepatoma cells increased the permeability of endothelial monolayers, but this effect was attenuated if exosome secretion of hepatoma cells was blocked by silencing ALIX and HRS or if miR‐103 within hepatoma or endothelial cells was antagonized. Most importantly, pretreating endothelial monolayers with exosomes that were from stable miR‐103‐expressing hepatoma cells facilitated the transendothelial invasion of tumor cells, and this role of exosomes was abrogated by inhibiting miR‐103 in endothelial cells. Further in vivo analyses disclosed that mice with xenografts of stable miR‐103‐expressing hepatoma cells exhibited higher vascular permeability in tumor, higher level of exosomal miR‐103 and greater number of tumor cells in blood circulation, and increased rates of hepatic and pulmonary metastases, compared to control mice. Mechanism investigations revealed that hepatoma cell‐secreted miR‐103 could be delivered into endothelial cells via exosomes, and then attenuated the endothelial junction integrity by directly inhibiting the expression of VE‐Cadherin (VE‐Cad), p120‐catenin (p120) and zonula occludens 1. Moreover, miR‐103 could also promote tumor cell migration by repressing p120 expression in hepatoma cells. Conclusion: Hepatoma cell‐secreted exosomal miR‐103 increases vascular permeability and promotes tumor metastasis by targeting multiple endothelial junction proteins, which highlights secreted miR‐103 as a potential therapeutic target and a predictive marker for HCC metastasis. (Hepatology 2018).
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•We derive optimal pre-hazard plans for interdependent systems resilience.•Failure uncertainty is accounted for by a two-stage adaptive robust optimization.•Uncertainty set is modeled based on ...components’ failure and repair probabilities.•The resulting trilevel model is solved by a nested decomposition algorithm.•Managerial insights are drawn from a specific case study.
This paper proposes a novel adaptive robust optimization (ARO)-based mathematical framework for resilience enhancement of interdependent critical infrastructure (CI) systems against natural hazards (NHs). In this framework, the potential impacts of a specific NH on an infrastructure are firstly evaluated, in terms of failure and recovery probabilities of system components; these are, then, fed into a two-stage ARO model to determine the optimal planning of resilience strategies under limited investment budget, anticipating the most-likely worst realization of the uncertainty of component failures under the NH. For its exact solution, a decomposition method based on simultaneous column-and-constraint generation is adopted. The approach is applied to a case study concerning the resilience of interdependent power and gas networks subject to (simulated) wind storms. The numerical results demonstrate the effectiveness of the proposed framework for the optimization of the resilience of interdependent CIs under hazardous events; this provides a valuable tool for making informed pre-hazard preparation decisions. The value of a coordinated pre-hazard planning that takes into account CI interdependencies is also highlighted.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Propose a stochastic programming for infrastructure restoration under uncertainty.•A multi-mode component repair model of higher practicality is considered.•Propose a tailored Benders decomposition ...to effectively solve the model.•Show the added value of the stochastic model against its deterministic counterpart.
The planning of post-disruption restoration in critical infrastructure systems often relies on deterministic assumptions such as complete information on resources and known duration of the repair tasks. In fact, the uncertainties faced by restoration activities, e.g. stemming from subjective estimates of resources and costs, are rarely considered. Thus, the solutions obtained by a deterministic approach may be suboptimal or even infeasible under specific realizations of the uncertainties. To bridge this gap, this paper investigates the effects of uncertain repair time and resources on the post-disruption restoration of critical infrastructure. Two-stage stochastic optimization provides insights for prioritizing the intensity and time allocation of the repair activities with the objective of maximizing system resilience. The inherent stochasticity is represented via a set of scenarios capturing specific realizations of repair activity durations and available resources, and their probabilities. A multi-mode restoration model is proposed that offers more flexibility than its single-mode counterpart. The restoration framework is applied to the reduced British electric power system and the results demonstrate the added value of using the stochastic model as opposed to the deterministic model. Particularly, the benefits of the proposed stochastic method increase as the uncertainty associated with the restoration process grows. Finally, decision-making under uncertainty largely impacts the optimum repair modes and schedule.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Health monitoring helps prevent, diagnose, and treat diseases, and has been given more and more attention in recent years. Triboelectric nanogenerators (TENGs) are promising for applications in ...health monitoring due to their myriad of merits including low cost, simple fabrication, light weight, self‐powered property, and wide selection of materials. Here, the recent key research achievements in TENG‐based health monitoring are comprehensively reviewed. TENGs have been applied to detect not only motion‐based health conditions such as pulse and heartbeat but also nonmotion‐based health conditions such as CO2 concentration and lactate concentration. The design of device structure, sensing mechanism, device performance, advantages and disadvantages of each kind of TENG‐based health monitors are discussed. Based on recent progresses, the existing challenges and future prospects for TENG‐based health monitoring are also discussed and summarized.
The recent key research achievements in triboelectric nanogenerator‐based health monitoring are comprehensively reviewed, and the existing challenges as well as future prospects are also thoroughly discussed and summarized.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
During the lockdown due to SARS-CoV-2 (coronavirus lockdown), there has been a tremendous increase in the number of students taking online courses. Few studies, however, have examined the individual ...dispositions that influence self-regulated online learning during the coronavirus lockdown. To address this gap, the present study explored the ineffectiveness of online learning and examined how it can be predicted by self-regulated online learning and participants' procrastination disposition. Data of 433 participants were collected and subjected to confirmatory factor analysis with structural equation modeling. The results indicated that procrastination is negatively related to 6 sub-constructs of self-regulated online learning: task strategy, mood adjustment, self-evaluation, environmental structure, time management, and help-seeking. These sub-constructs were negatively related to the learners' perceived ineffectiveness of online learning. However, the relationship between perceived learning ineffectiveness and environmental structure or help-seeking was weaker than that with task strategy or mood adjustment, indicating that the latter two subtypes of self-regulated online learning should be considered before students engage in online learning.
•Procrastination, self-regulated online learning (SRL) and ineffectiveness on COVID-19•Procrastination was negatively related to all sub-constructs of SRL.•SRL was negatively associated with perceived ineffectiveness of online learning.•Mood adjustment had the most negative relationship with learning ineffectiveness.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Macroautophagy (hereafter called autophagy) is a highly conserved physiological process that degrades over-abundant or damaged organelles, large protein aggregates and invading pathogens via the ...lysosomal system (the vacuole in plants and yeast). Autophagy is generally induced by stress, such as oxygen-, energy- or amino acid-deprivation, irradiation, drugs,
. In addition to non-selective bulk degradation, autophagy also occurs in a selective manner, recycling specific organelles, such as mitochondria, peroxisomes, ribosomes, endoplasmic reticulum (ER), lysosomes, nuclei, proteasomes and lipid droplets (LDs). This capability makes selective autophagy a major process in maintaining cellular homeostasis. The dysfunction of selective autophagy is implicated in neurodegenerative diseases (NDDs), tumorigenesis, metabolic disorders, heart failure,
. Considering the importance of selective autophagy in cell biology, we systemically review the recent advances in our understanding of this process and its regulatory mechanisms. We emphasize the 'cargo-ligand-receptor' model in selective autophagy for specific organelles or cellular components in yeast and mammals, with a focus on mitophagy and ER-phagy, which are finely described as types of selective autophagy. Additionally, we highlight unanswered questions in the field, helping readers focus on the research blind spots that need to be broken.
Nitrogen accounts for approximately 60% of the fertilizer consumed each year; thus, it represents one of the major input costs for most nonlegume crops. Nitrate is one of the two major forms of ...nitrogen that plants acquire from the soil. Mechanistic insights into nitrate transport and signaling have enabled new strategies for enhancing nitrogen utilization efficiency, for lowering input costs for farming, and, more importantly, for alleviating environmental impacts (e.g., eutrophication and production of the greenhouse gas N
2
O). Over the past decade, significant progress has been made in understanding how nitrate is acquired from the surroundings, how it is efficiently distributed into different plant tissues in response to environmental changes, how nitrate signaling is perceived and transmitted, and how shoot and root nitrogen status is communicated. Several key components of these processes have proven to be novel tools for enhancing nitrate- and nitrogen-use efficiency. In this review, we focus on the roles of NRT1 and NRT2 in nitrate uptake and nitrate allocation among different tissues; we describe the functions of the transceptor NRT1.1, transcription factors, and small signaling peptides in nitrate signaling and tissue communication; and we compile the new strategies for improving nitrogen-use efficiency.
The increasing penetration of grid-enabled electric vehicles (EVs) renders road networks (RNs) and power networks (PNs) increasingly interdependent for normal operation. For this reason, recently few ...studies have started to investigate the vulnerability of a highly coupled traffic-power system in the presence of disruptive events. Actually, however, only very few of these studies have considered the impact of EVs on the interdependent traffic-power system during restoration from a disruptive event. In an attempt to fill this gap, in this study, we investigate the restoration planning of both independent RNs and PNs, and interdependent traffic-power systems. A mixed integer program model is formulated to provide optimal reconfiguration and operational solutions for post-disruption traffic-power systems recovery. The objective of the model is to minimize the total cost incurred by system performance loss, which is quantified by the cumulative unmet traffic demand for RNs and load shedding cost for PNs. Several reconfiguration strategies are considered, including links reversing in RNs and line switching in PNs, to optimize system resilience. In the proposed model, the integrated problem of system optimal dynamic traffic assignment and optimal power flow is solved to derive the optimal traffic-power flow. RNs and PNs are coupled through the coordinately allocated spatio-temporal charging demand of EVs. A partial highway network in North Carolina (NC), USA, and a modified IEEE-14 bus system are used to illustrate the application of the model. The numerical results obtained show the added value of coordinately planning restoration for traffic-power systems and the effects of different levels of EV penetration.
•Emergency response of traffic-power systems coupled via grid-enabled electric vehicles.•Explicitly model the interdependency between the two coupled systems.•Originally formulate reconfiguration strategies for the coupled networks.•Coordinately planning the emergency response restores performance more effectively.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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