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Nanoparticles can acquire a plasma protein corona defining their biological identity. Corona functions were previously considered for cell‐derived extracellular vesicles (EVs). Here we demonstrate that nano‐sized EVs from therapy‐grade human placental‐expanded (PLX) stromal cells are surrounded by an imageable and functional protein corona when enriched with permissive technology. Scalable EV separation from cell‐secreted soluble factors via tangential flow‐filtration (TFF) and subtractive tandem mass‐tag (TMT) proteomics revealed significant enrichment of predominantly immunomodulatory and proangiogenic proteins. Western blot, calcein‐based flow cytometry, super‐resolution and electron microscopy verified EV identity. PLX‐EVs partly protected corona proteins from protease digestion. EVs significantly ameliorated human skin regeneration and angiogenesis in vivo, induced differential signalling in immune cells, and dose‐dependently inhibited T cell proliferation in vitro. Corona removal by size‐exclusion or ultracentrifugation abrogated angiogenesis. Re‐establishing an artificial corona by cloaking EVs with fluorescent albumin as a model protein or defined proangiogenic factors was depicted by super‐resolution microscopy, electron microscopy and zeta‐potential shift, and served as a proof‐of‐concept. Understanding EV corona formation will improve rational EV‐inspired nano‐therapy design.

This chapter discusses computing the EV/EBITDA ratio from factors that drive free cash flow. The EV/EBITDA and the P/E ratios are similarly driven by growth, cost of capital, and return, but the EV/EBITDA must also account for capital expenditures, taxes, and working capital. In computing the EV/EBITDA ratio, functions that establish the stable ratio of net plant to depreciation and the stable ratio of capital expenditures to depreciation make the process manageable. A model can be made that begins with invested capital balance and then derives NOPAT, EBIT, EBITDA, and free cash flow. Movement in the invested capital balance requires establishing depreciation expense and capital expenditures, both of which can be established from user‐defined functions. A function can be created to compute the implied EV/EBITDA that accepts plant life, tax rate, growth rate, rate of return, and cost of capital. A comprehensive analysis includes working capital and deferred taxes as well as capital expenditures. This function is an aggregation of concepts from other chapters and requires analysis of prospective retirements driven by the age of plant. The comprehensive analysis of EV/EBITDA from value drivers can result in wide differences with valuation that comes from the value driver formula or the growth rate analysis.

Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan’s situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries.

Studying specific subpopulations of cancer-derived extracellular vesicles (EVs) could help reveal their role in cancer progression. In cancer, an increase in reactive oxygen species (ROS) happens which results in lipid peroxidation with a major product of 4-hydroxynonenal (HNE). Adduction by HNE causes alteration to the structure of proteins, leading to loss of function. Blebbing of EVs carrying these HNE-adducted proteins as a cargo or carrying HNE-adducted on EV membrane are methods for clearing these molecules by the cells. We have referred to these EVs as Redox EVs. Here, we utilize a surface tension-mediated extraction process, termed exclusion-based sample preparation (ESP), for the rapid and efficient isolation of intact Redox EVs, from a mixed population of EVs derived from human glioblastoma cell line LN18. After optimizing different parameters, two populations of EVs were analyzed, those isolated from the sample (Redox EVs) and those remaining in the original sample (Remaining EVs). Electron microscopic imaging was used to confirm the presence of HNE adducts on the outer leaflet of Redox EVs. Moreover, the population of HNE-adducted Redox EVs shows significantly different characteristics to those of Remaining EVs including smaller size EVs and a more negative zeta potential EVs. We further treated glioblastoma cells (LN18), radiation-resistant glioblastoma cells (RR-LN18), and normal human astrocytes (NHA) with both Remaining and Redox EV populations. Our results indicate that Redox EVs promote the growth of glioblastoma cells, likely through the production of H2O2, and cause injury to normal astrocytes. In contrast, Remaining EVs have minimal impact on the viability of both glioblastoma cells and NHA cells. Thus, isolating a subpopulation of EVs employing ESP-based immunoaffinity could pave the way for a deeper mechanistic understanding of how subtypes of EVs, such as those containing HNE-adducted proteins, induce biological changes in the cells that take up these EVs.

Extracellular vesicles (EVs), including exosomes and microvesicles, are extracellular nanovesicles released by most cells. EVs play essential roles in intercellular communication via the transport of a large variety of lipids, proteins, and nucleic acids to recipient cells. Nucleic acids are the most commonly found molecules inside EVs, and due to their small size, microRNAs and other small RNAs are the most abundant nucleic acids. However, longer molecules, such as messenger RNAs (mRNAs), have also been found. mRNAs encapsulated within EVs have been shown to be transferred to recipient cells and translated into proteins, altering the behavior of the cells. Secretion of EVs is maintained not only through multiple normal physiological conditions but also during aberrant pathological conditions, including cancer. Recently, the mRNAs carried by EVs in cancer have attracted great interest due to their broad roles in tumor progression and microenvironmental remodeling. This review focuses on the biological functions driven by mRNAs carried in EVs in cancer, which include supporting tumor progression by activating cancer cell growth, migration, and invasion; inducing microenvironmental remodeling via hypoxia, angiogenesis, and immunosuppression; and promoting modulation of the microenvironment at distant sites for the generation of a premetastatic niche, collectively inducing metastasis. Furthermore, we describe the potential use of mRNAs carried by EVs as a noninvasive diagnostic tool and novel therapeutic approach.

Active learning (AL) is a powerful method for accelerating novel materials discovery but faces huge challenges for extracting physical meaning. Herein, we novelly apply an interpretable AL strategy to efficiently optimize the photothermal conversion efficiency (PCE) of carbon dots (CDs) in photothermal therapy (PTT). An equivalent value (SHapley Additive exPlanations equivalent value [SHAP‐EV]) is proposed which explicitly quantifies the linear contributions of experimental variables to the PCE, derived from the joint SHAP values. The SHAP‐EV, with an R2 of 0.960 correlated to feature's joint SHAP, is integrated into the AL utility functions to enhance evaluation efficiency during optimization. Using this approach, we successfully synthesized iron‐doped CDs (Fe‐CDs) with PCE exceeding 78.7% after only 16 experimental trials over four iterations. This achievement significantly advances the previously low PCE values typically reported for CDs. Furthermore, Fe‐CDs demonstrated multienzyme‐like activities, which could respond to the tumor microenvironment (TME). In vitro and in vivo experiments demonstrate that Fe‐CDs could enhance ferroptosis through synergistic PTT and chemodynamic therapy (CDT), thereby achieving remarkable antitumor efficacy. Our interpretable AL strategy offers new insights for accelerating bio‐functional materials development in antitumor treatments. On the basis of active learning strategy, we propose an equivalent value—SHapley Additive exPlanations equivalent value—to optimize carbon dots’ photothermal conversion efficiency. Within four iterations, iron‐doped carbon dots are synthesized with the efficiency exceeding 78.7%. Furthermore, the final functional nanomaterial demonstrates multienzyme‐like activities and enhances ferroptosis through synergistic photothermal therapy and chemodynamic therapy, achieving remarkable antitumor efficacy.

The increasing environmental awareness paired with the rise of global warming effects has led, in the past few years, to an increase in the sales of electric vehicles (EVs), partly but not only, caused by governmental incentives. A significant roadblock in the mass transition to EVs can be found in the so-called range anxiety: not only do EVs have, generally, considerably shorter ranges than their internal combustion engine vehicle (ICEV) equivalents, but recharge takes significantly longer than does filling up a gas tank, and charging stations are less widespread than are petrol stations. To counteract this, EV manufacturers are developing route planners which select the best route to go from A to B according to the range of the vehicle and the availability of charging stations. These tools are indeed powerful but do not account for the state of health (SoH) of the battery or for temperature conditions, two factors which may severely degrade the range of an EV. This article presents an innovative route planning method which takes into account SoH, temperature and driving style and selects, along the planned route, the charging stations among those which can be reached with the energy of the battery. To verify its proper operativity, simulations were conducted, highlighting the risk of running out of battery before destination, considering if the route is planned based on the declared range, and taking into account battery SoH, external temperature and driving style.

In the lithium-ion battery (LIB) supply-chain, transactions involve several rounds of ordering, production and delivery between LIB suppliers and electric vehicle (EV) manufacturers. The sustainable performance of LIB suppliers, related to various characteristics, significantly affects the participants’ sustainable reputations. The EV-LIB supply-chain transaction mechanism is explored from the perspective of the exchange economy comprehensively addressing both short-term economic profit and long-term sustainable reputation. Specifically, a “profit-reputation” utility function is proposed to reflect participants’ expectations regarding cooperation profit and sustainable reputation. Additionally, an Edgeworth box model is developed to describe the participant’s balance determinations as a contract curve, revealing the Pareto conditions for mutually beneficial transactions based on sustainable performance. Furthermore, several principal-agent models are established to analyze the equilibrium of sustainable transactions within the EV-LIB supply-chain under varying dominance scenarios. A case study of an EV-LIB transaction is conducted to demonstrate the feasibility and effectiveness. This study aims to assist supply chain managers, researchers and decision-makers in exploring the role of participant’s sustainable reputation and its influence on supply-chain transaction and equilibrium, particularly in the context of designing cooperative contracts and negotiation process to foster sustainable supply chains.

Extracellular vesicles (EVs) are highly heterogeneous, and different EV subpopulations from various origins mediate different biological effects. The separation of different subpopulations of EVs from mixtures is critical but challenging. Epididymosomes are secreted by the epididymal epithelium and play a key role in sperm maturation and function. However, limited access to human epididymal tissue and epididymal fluid has hampered further study of epididymosomes and their potential clinical applications. Here, we established a novel strategy based on flow cytometry sorting to isolate human CD63‐positive epididymosomes from ejaculate. We identified CD52, a membrane‐located protein expressed exclusively in the epididymis, as the sorting marker for human epididymosomes. Then, CD63‐positive epididymosomes were isolated from human semen using a flow cytometry sorting instrument and concentrated. Additionally, we observed that isolated CD63‐positive epididymosomes improved sperm function more than other CD63‐positive seminal EV subpopulations did, demonstrating the successful isolation of a subpopulation of epididymosomes from human semen and their potential application in the clinic.

As the adoption of electric vehicles (EVs) rapidly increases, the expansion of charging infrastructure has become a critical issue. Unlike internal combustion engine vehicles, EV charging is sensitive to factors such as the time and location for charging, depending on the charging speed and capacity of the battery. Therefore, recommending an appropriate charging station that comprehensively considers not only the user’s preference but also the charging time, waiting time, charging fee rates, and power supply status is crucial for the user’s convenience. Currently, charging station recommendation services suggest suitable charging stations near a designated location and provide information on charging capacity, fee rates, and availability of chargers. Furthermore, research is being conducted on EV charging station recommendations that take into account various charging environments, such as power grid and renewable energy conditions. To solve these optimization problems, a large amount of information about the user’s history and conditions is required. In this paper, we propose a real-time charging station recommendation method based on minimal and simple current information while driving to the destination. We first propose a preference objective function that considers the factors of distance, time, and fees, and then analyze the recommendation results based on both synthetic and real-world charging environments. We also observe the recommendation results for different combinations of the weights for these factors. If we set all the weights equally, we can obtain appropriate recommendations for charging stations that reflect driving distance, trip time, and charging fees in a balanced way. On the other hand, as the number of charging stations in a given area increases, it has been found that gradually increasing the weighting of charging fees is necessary to alleviate the phenomenon of rising fee rates and provide balanced recommendations.