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Abnormal angiogenesis is associated with myriad human diseases including proliferative diabetic retinopathy. Signaling transduction via phosphoinositide 3-kinases (PI3Ks) plays a critical role in angiogenesis. Herein, we showed that p110δ, the catalytic subunit of PI3Kδ, was highly expressed in pathological retinal vascular endothelial cells (ECs) in a mouse model of oxygen-induced retinopathy (OIR) and in fibrovascular membranes from patients with proliferative diabetic retinopathy. To explore novel intervention with PI3Kδ expression, we developed a recombinant dual adeno-associated viral (rAAV) system for delivering CRISPR/Cas9 in which Streptococcus pyogenes (Sp) Cas9 expression was driven by an endothelial specific promoter of intercellular adhesion molecule 2 (pICAM2) to edit genomic Pik3cd, the gene encoding p110δ. We then demonstrated that infection of cultured mouse vascular endothelial cells with the dual rAAV1s of rAAV1-pICAM2-SpCas9 and rAAV1-SpGuide targeting genomic Pik3cd resulted in 80% DNA insertion/deletion in the locus of genomic Pik3cd and 70% depletion of p110δ expression. Furthermore, we showed that in the mouse model of OIR editing retinal Pik3cd with the dual rAAV1s resulted in not only a significant decrease in p110δ expression, and Akt activation, but also a dramatic reduction in pathological retinal angiogenesis. These findings reveal that Pik3cd editing is a novel approach to treating abnormal retinal angiogenesis.

Existing capacity-based network pricing uses discounted cash flows to calculate costs, unable to reflect the uncertainties and flexibilities in distribution networks. Such shortcoming could distort the cost-reflectivity of pricing signals, particularly those for renewables and flexible technologies, causing more constraints and curtailment issues in networks. This paper proposes a new pricing method, Incremental Cost Network Pricing based on Real Options (ICOC), which can reflect network user uncertainties on network investment by using real options. Under this concept, network operators can delay investment for a certain period by paying waiting cost based on options value until more information is available, thus avoiding non-reversible investment due to uncertainties. The options cost will be levied on network users as i) rewards if they can provide flexibilities to the system; or ii) waiting costs if they present uncertainties to the system. The reward or cost is determined by a binomial tree pricing under a risk-neutral condition, which is added onto asset present value as the total cost to be recovered. Such cost is allocated to network users based on their nodal incremental costs. The proposed method is demonstrated on a practical network with different users, i) uncertain, ii) flexible; iii) certain and nonflexible.

Masses of user-side smart meter data provide a foundation for utilities to acquire users’ electricity consumption patterns and promote refined energy management. Therefore, the charging load of electric vehicles can be identified non-intrusively through the smart meter data of residential users, which provides flexible resources for the demand side response of power grid enterprises and is beneficial to the power dispatching decision making. Meanwhile, revealing residential energy consumption behavior from smart meter data is treated as energy privacy leakage. Extensive data collection from smart meters has raised privacy risk, which could lead to the reduction of trust in utilities from households. Aiming at this problem, the paper proposes a non-intrusive method to extract residential electric vehicles, and on this basis, discusses the privacy protection. In this paper, DWT method is used to decompose the total power of smart meter to extract the charging load of electric vehicle, and similar charging load patterns are added to smart meter data to preserve the residential EV charging behavior privacy. Practical households with EVs are used to test the proposed method and the result shows its feasibility for the performance of privacy-preserving.

This paper addresses the cyber resilience issues of multi-vector energy distribution systems (MEDS) caused by false data injection FDI, considering the uncertainty from renewable resources. A novel two-stage distributionally robust optimization (DRO) is proposed to realize the day-ahead and real-time resilience improvement. The first stage determines an initial plan for day-ahead reserve preparation and the second stage makes adjustment and takes resilience-based actions after potential load redistribution (LR) attacks and renewable output deviation. The ambiguity set is based on both the Wasserstein distance and moment information. Compared to robust optimization which considers the worst case, DRO yields less-conservative solutions and thus provides more economic operation schemes. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Case studies are demonstrated on two representative MEDS networked with energy hubs, i.e., a 33-bus-20-node MEDS and a 69-bus-20-node-MEDS, illustrating the effectiveness of the proposed cyber-secured model.

The CRISPR/Cas9 system has been harnessed to cleave a targeted DNA fragment via its Cas nuclease activity under the direction of guide RNA for rendering gene insertions, deletions, and point mutations in basic research and clinical applications. There are a number of vehicles, including lipofectamine, viruses, and nanoparticles, that can deliver the CRISPR/Cas9 system, but all these methods face numerous challenges during their application in life science contexts. Here, we focus on the delivery of CRISPR/Cas9 via nanoparticles because this method has shown great advantages in terms of safety, simplicity and flexibility.

Traditional system investment decision is costly and hard to reverse. This is aggravated by uncertainties from flexible load and renewables (FLR), which impact the accuracy of network investment decisions and could trigger a high asset risk. Thus, system operators have the incentive to postpone network reinforcement, and ‘wait and see’ whether the request of investment can be reduced or delayed with new information. This paper proposes a novel method to evaluate network investment horizon deferral based on the trade-off between waiting profit and waiting cost under FLR uncertainties. Although deferring investment leads to waiting cost, it is worthy to wait if the cost is smaller than the waiting profits. To capture the impact of FLR uncertainties on system investment, nodal uncertainties are converted into branch flow uncertainties based on a combined cumulant and Gram-Charlier expansion method. The waiting cost is quantified by the options’ cost based on real options method and waiting profit is from asset present value reduction due to the deferral. Thus, by paying waiting cost, current investment cost can be reserved until uncertainties are reduced to an acceptable level. The waiting time is evaluated by Sharp ratio and expected return, determined by the waiting cost and uncertainty level. The results show that paying waiting cost is an economical way to reduce the impact of uncertainty and avoid hastily investment.

Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model’s effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water–energy–social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

We report the clinical features, treatment strategies and outcomes in a series of patients with infectious endophthalmitis after cataract surgery caused by Cutibacterium acnes (C. acnes), formerly known as Propionibacterium acnes (P. acnes). This retrospective case series includes six eyes of six patients with chronic postoperative endophthalmitis caused by culture-proven C. acnesfrom December 2010 to July 2019 at a University referral center. All patients underwent prior cataract extraction with intraocular lens (CE/IOL) implantation. The mean time between cataract surgery and the microbiologic diagnosis of endophthalmitis was 7.4 ± 5.2 months (range 1.5–17 months). The average time from obtaining the specimen to culture positivity was 7.7 ± 4.4 days (range 3–15 days). Three eyes (50%) presented with hypopyon and three eyes (50%) presented with prominent keratic precipitates without hypopyon. Presenting visual acuity ranged from 20/25 to 2/200. Initial treatments included intravitreal antibiotics alone (n = 2), pars plana vitrectomy (PPV) with partial capsulectomy and intravitreal antibiotics (n = 3), and pars plana vitrectomy with IOL removal and intravitreal antibiotics (n = 1). Follow-up treatments included IOL removal (n = 2), intravitreal antibiotics (n = 1), and topical antibiotics (n = 1). The best-corrected visual acuity at last follow-up was 20/70 or better in all patients. In a literature review, the clinical features and treatment outcomes for all case series of delayed-onset postoperative endophthalmitis caused by C. acnes(n = 120) are listed. A definitive cure (the absence of recurrent inflammation) was achieved in 100% of patients that underwent IOL removal, in 77% of those that underwent PPV/partial capsulectomy and intravitreal antibiotics, and in 18% of cases treated with intravitreal antibiotics alone. Endophthalmitis after CE/IOL caused by C. acnesis characterized by slowly progressive intraocular inflammation and has a protracted course from surgery to microbiologic diagnosis. Visual outcomes are generally favorable, but IOL explantation may be necessary for definitive cure.

The future energy system embraces growing flexible demand and generation, which bring large-scale uncertainties and challenges to current deterministic network pricing methods. This paper proposes a novel reliability-based probabilistic network pricing method considering demand uncertainty. Network reliability performance, including probabilistic contingency power flow (PCPF) and tolerance loss of load (TLoL), are used to assess the impact of demand uncertainty on actual network investment cost, where PCPF is formulated by the combined cumulant and series expansion. The tail value at risk (TVaR) is used to generate analytical solutions to determine network reinforcement horizons. Then, final network charges are calculated based on the core of the Long-run incremental cost (LRIC) algorithm. A 15-bus system is employed to demonstrate the proposed method. Results indicate that the pricing signal is sensitive to both demand uncertainty and network reliability, incentivising demand to reduce uncertainties. This is the first-ever network pricing method that determines network investment costs considering both supply reliability and demand uncertainties. It can guide better sitting and sizing of future flexible demand in distribution systems to minimise investment costs and reduce network charges, thus enabling a more efficient system planning and cheaper integration.

Power system operation faces an increasing level of uncertainties from renewable generation and demand, which may cause large-scale congestion under an ineffective operation. This article applies energy storage (ES) to reduce system peak and the congestion by the robust optimization, considering the uncertainties from the ES state-of-charge (SoC), flexible load, and renewable energy. First, a deterministic operation model for the ES, as a benchmark, is designed to reduce the variance of the branch power flow based on the least-squares concept. Then, a robust model is built to optimize the ES operation with the uncertainties in the severest case from the load, renewable energy, and ES SoC that are converted into branch flow budgeted uncertainty sets by the cumulant and Gram–Charlier expansion methods. The ES SoC uncertainty is modeled as an interval uncertainty set in the robust model, solved by the duality theory. These models are demonstrated on a grid supply point to illustrate the effectiveness of a congestion management technique. Results illustrate that the proposed ES operation significantly improves system performance in reducing the system congestion. This robust optimization-based ES operation can further increase system flexibility to facilitate more renewable energy and flexible demand without triggering the large-scale network investment.

The aim of this study was to examine the functional connectivity between the primary visual cortex and other cortical areas during rest in normal subjects and patients with comitant strabismus using functional magnetic resonance imaging (fMRI).A prospective, observational study was conducted. Ten patients with comitant exotropia and eleven matched healthy subjects underwent resting-state fMRI with their eyes closed. Resting-state fMRI was performed using a 3.0 T MR scanner. The primary visual cortex was subdivided into anterior and posterior subdivisions. The resting-state functional connectivities within the primary visual cortex and between the primary visual cortex and other cortical areas were calculated for each group and compared between the strabismic and normal control groups. fMRI data were analyzed using Statistical Parametric Mapping software and Analysis of Functional NeuroImages software.Compared with the normal controls, patients with comitant strabismus had increased functional connectivity between the posterior primary visual cortex and other cortical areas, especially the visual cortex [Brodmann area 19 (BA19)] and other oculomotor regions, such as the frontal eye field (BA6).The fMRI results suggest that ongoing and permanent cortical changes occur in patients with comitant strabismus. Disrupted brain functional connectivities are associated with abnormal eye movement and loss of stereopsis. Our study provides a neurological basis for understanding the pathophysiology of comitant strabismus, which may prompt new areas of research to more precisely define this basis and extend these findings to enhance diagnosis and treatment.

Amblyopia is visual impairment characterized by a structurally normal eye showing significantly lower visual acuity than the fellow eye. Traditional treatment of amblyopia includes patching the good eye to force the amblyopic eye to work during normal daily activities; however, this approach is are limited by low compliance. Recently, researchers proposed a new treatment for amblyopia: video game playing. In the current review, we discuss whether video game playing can treat amblyopia, whether video game playing could better treat amblyopia than traditional treatments, and how the video game treats amblyopia and its possible mechanism. Based on results from our literature review and meta-analysis, we suggest there is strong evidence for the effectiveness of video game treatments. Moreover, multiple factors within and outside of video games could influence the treatment effect.

A fixation preference for the affected eye is uncommon in patients with unilateral Duane retraction syndrome (DRS), and surgery on the fellow eye is rarely advocated. We are presenting a case report of a 9-year-old boy with unilateral DRS type II in the left eye who received lateral rectus muscle recession in his right amblyopic eye. The patient was orthophoric and his face turn was gone 6 months postoperatively. Surgery on the fellow amblyopic eye is a good choice for unilateral DRS where the affected eye dominants the fixation, and the satisfactory outcome suggests that alignment in the primary position can correct the face turn effectively despite the muscle duction deficit in the affected eye and further extend the binocular single visual field.

Facilitated by advanced information and communication technologies (ICTs), local energy trading develops rapidly, playing an important role in the energy supply chain. Thus, it is essential to develop local trading models and strategies that can benefit participants, not only stimulating local balancing but also promoting renewable penetration. This paper proposes a new local energy trading decision-making model for suppliers by using the Cournot Oligopoly game, considering the uncertainty costs of renewable energy. Four types of representative energy providers are modelled, traditional thermal generation, wind power, photovoltaic (PV) power, and electricity storage. The revenue of these technologies is extensively formulated according to their operation cost, investment cost, and income from selling energy. The uncertainty cost of renewable generation is integrated into the trading, modelled as a penalty for potential energy shortage that is derived from output probability distribution function (PDF). This trading model is formulated as a non-cooperative Cournot oligopoly game to enable energy suppliers to maximize their profits through local trading considering price. The response of the customer to energy price variations, i.e. demand elasticity, is also included in the model. A unique Nash equilibrium (NE) and optimum strategies are derived by the proposed Optimal-Generation-Plan (OGP) Algorithm. As demonstrated in a typical local market, the proposed approach can effectively model and resolve multiple suppliers’ competition in local energy trading. It can work as a vehicle to facilitate the trading between various generation technologies and customers, realising local balancing and benefiting all market participants with enhanced revenue and reduced energy bills.

This paper proposes an innovative two-stage data-driven optimization framework for a multi-energy system. Enormous energy conversion technologies are incorporated in the system to enhance the overall energy utilization efficiency, i.e., combined heat and power, power-to-gas, gas furnace, and ground source heat pump. Furthermore, a demand response program is adopted for stimulating the load shift of customers. Accordingly, both the economic performance and system reliability can be improved. The endogenous solar generation brings about high uncertainty and variability, which affects the decision making of the system operator. Therefore, a two-stage data-driven distributionally robust optimization (TSDRO) method is utilized to capture the uncertainty. A tractable semidefinite programming reformulation is obtained based on the duality theory. Case studies are implemented to demonstrate the effectiveness of applying the TSDRO on energy management.

Background: Bietti crystalline corneoretinal dystrophy (BCD) is an autosomal recessive retinal dystrophy which is caused by the mutations of CYP4V2.Here we identified new CYP4V2compound heterozygous mutations in BCD.Methods:381 pathogenic genes related to retinal diseases were screened by targeted sequence capture array techniques and confirmed by Sanger sequencing.Results:Two female siblings with BCD carry two compound heterozygous mutations in CYP4V2. One was missense mutation c.1198C>T (p.R400C) and the other was frameshift mutation c.802-8_810delinsGC (p.V268_E329del).Optical coherence tomography (OCT) showed that the ellipsoid zone was absent in the macular regions and electroretinogram (ERG) revealed poor cone and rod responses. Conclusions:Newcompound heterozygous mutations in CYP4V2 are related to the BCD.Our study expands our knowledge of heterogenic phenotypes and genotypes through genetic diagnosis of the BCD patients.

Purpose: To examine stereopsis in patients with comitant horizontal strabismus who had normal vision and were stereoblind following the strabismus surgery. Methods: Twenty patients with primary comitant horizontal strabismus and normal corrected visual acuity were included in this study. The stereoacuity was quantitatively measured by the random dot butterfly stereotest, the Randot Preschool Stereoacuity Test (Stereo Optical, Inc., Chicago, IL), and synoptophore. Results: Twenty patients with comitant strabismus (14 exotropia and 6 esotropia) with normal corrected visual acuity but without stereopsis were included. The average prism deviation was 63.55 ± 18.52 prism diopters (PD) (range: 30 to 90 PD). The mean duration of strabismus was 17.93 ± 7.0 years (range: 5 to 30 years). At 3 days postoperatively, 22.2% (4 of 18) of the patients had improved stereopsis and the mean stereoacuity was 60 ± 28.28 seconds of arc by random dot butterfly stereotest and 150 ± 57.74 seconds of arc by Randot Preschool Stereoacuity Test ( P < .05). For the fusion and stereopsis at far measured by the synoptophore, 11.1% (2 of 18) of the patients demonstrated recovery of fusion and substantial recovery of stereopsis. The mean stereoacuity (at far) was 200 seconds of arc. At the last follow-up visit (3 to 12 months, average 6.55 ± 3.46 months), 45% (9 of 20) of the patients demonstrated substantial near stereopsis recovery and the median stereoacuity was 53.33 ± 33.17 seconds of arc by random dot butterfly stereotest and 95.56 ± 63.07 seconds of arc by the Randot Preschool Stereoacuity Test ( P < .01). A total of 25% (5 of 20) of the patients demonstrated recovery of fusion and 15% (3 of 20) of the patients showed substantial recovery of stereopsis at far. The median stereoacuity (at far) was 193.33 ± 179.26 seconds of arc. Conclusions: The results demonstrated that adult patients with comitant strabismus who had good vision and were stereoblind benefited from strabismus surgery and the stereopsis improved. Near stereopsis was easier to regain than far stereopsis. [ J Pediatr Ophthalmol Strabismus . 2018;55(6):407–411.]

Renewable energy is the key to meeting increasing electricity demand and the decarburization targets in the generation mix. However, due to constrained power network capacity, a large volume of renewable generation is curtailed particularly from wind power, which is a huge waste of resources. There are typically three approaches to addressing excessive renewable: direct curtailment, the reinforcement of networks to expand transfer capacity, and the conversion of excessive renewable into other energy types, such as hydrogen, to transport. The costs and benefits of the three approaches could vary significantly across location, time, and penetration of renewable energy. This paper conducts a cost-benefit analysis and comparison of the three techniques to address wind curtailment. It uses a reduced 16-busbar UK transmission network to analyze the performance of the three approaches. The UK 2020 generation mix is used to quantify the saved renewable energy and incurred costs. The payback time and net present value of the two investment techniques are compared. From demonstration, it is reasonable to conclude that converting excessive wind power into hydrogen to transport is an environmentally friendly and cost-effective way to address wind curtailment.

Network pricing is essential for electricity system operators to recover investment and operation costs from network users. Current pricing schemes are only for generation and demand that purely withdraws or injects power from/into the system. However, they cannot properly price energy storage (ES), which has the dual characteristics of injecting and withdrawing power. This paper develops a novel pricing scheme for ESs in distribution systems operated by customers to reflect their impact on network planning and operation. A novel charging and discharging methodology is designed for ESs to respond to time of use tariffs for maximising electricity cost savings. The long-term incremental cost for ES is designed based on future reinforcement horizon and short-term operation cost is quantified by system congestion. Then, a novel pricing scheme for ES is designed by integrating the two costs. The pricing signals can guide ES operation to benefit both distribution network operators and ES owners. The new methodology is demonstrated on a small system with an ES of different features and then on a practical Grid Supply Point (GSP) area.

The integration of distributed energy resources facilitates peer-to-peer (P2P) energy sharing as an effective way to coordinate the energy scheduling. Previous research has focused on economic P2P energy sharing of user side without considering the possible response strategies of network sides. This paper proposes a P2P energy sharing framework that takes into consideration the dynamic network structure. A P2P energy sharing model aimed at increasing the energy local consumption and reducing each prosumers’ power losses arisen from P2P energy sharing is built for the P2P energy schedule. In the physical network, a dynamic network structure model is designed to incorporate the network operator into the energy sharing process, and obtain the better network structure while reducing the power losses of whole network. These two proposed models are jointly optimized by the upper and lower layer to get the optimal P2P energy sharing schedule, network operations conditions and comprehensive energy utilization. The solution algorithm for the joint optimization is composed of a designed matching mechanism and branch-exchange method and realized by the iteration process. Finally, numerical analysis reveals the effectiveness of the proposed framework in terms of prosumers’ strategies, network structures, comprehensive energy utilization, and practical feasibility.

This paper designs a novel dynamic tariff scheme for demand response (DR) by considering networks costs through balancing the trade-off between network investment costs and congestion costs. The objective is to actively engage customers in network planning and operation for reducing network costs and finally their electricity bills. System congestion costs are quantified according to generation and load curtailment by assessing their contribution to network congestion. Plus, network investment cost is quantified through examining the needed investment for resolving system congestion. Customers located at various might face the same energy signals but they are differentiated by network cost signals. Once customers conduct DR during system congested periods, the smaller savings from investment and congestion cost are considered as the economic singles for rewarding the response. The innovation is that the method translates network congestion/investment costs into tariffs, where current research is mainly focused on linking customer response to energy prices. A typical UK distribution network is utilised to illustrate the new approach and results show that derived economic signals can effectively benefit end customers for reducing system congestion costs and deferring required network investment.

Energy storage (ES) is playing a vital role in providing multiple services in several electricity markets. However, the benefits and risks vary across markets and time, which justifies the importance to optimize ES capacity share in different markets. In this paper, a novel portfolio theory-based approach is proposed for optimally managing ES in various markets to maximize benefits and reduce the risk for ES owners. Three markets are considered, which are energy arbitrage, ancillary services, and distributed network operator's market. They are modeled based on energy cost, frequency response cost, and system congestion cost. Portfolio theory is utilized to quantify ES capacity allocated to each market over time for various levels of risk aversions. The relation between risks and expected return of different markets is efficiently reflected by the portfolio theory, providing implications to storage operation. The extensive demonstration illustrates that the markets that storage can participate in are fundamentally different regarding to its risk aversion. In addition, the optimum portfolio of the markets for storage is on the efficient frontier, providing the maximum return at a certain risk aversion level. This study is particularly useful for guiding market participation and operation of ES to gain maximum economic return at minimum risk.