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Electricity market participants rely on data-driven methods using public market data to predict locational marginal prices (LMPs) and determine optimal bidding strategies, since they cannot access confidential power system models and operating details. In this paper, system-wide heterogeneous public market data are organized into a 3-dimensional (3D) tensor, which can store their spatio-temporal correlations. A generative adversarial network (GAN)-based approach is proposed to predict real-time locational marginal prices (RTLMPs) by learning the spatio-temporal correlations stored in the historical market data tensor. An autoregressive moving average (ARMA) calibration method is adopted to improve the prediction accuracy. Case studies using public market data from Midcontinent Independent System Operator (MISO) and Southwest Power Pool (SPP) demonstrate that the proposed method is able to learn spatio-temporal correlations among RTLMPs and perform accurate RTLMP prediction.

Trends in the development of modern medicine necessitate the efficient delivery of therapeutics to achieve the desired treatment outcomes through precise spatiotemporal accumulation of therapeutics at the disease site. Bioresponsive nanomedicine is a promising platform for this purpose. Dynamic covalent bonds (DCBs) have attracted much attention in studies of the fabrication of bioresponsive nanomedicines with an abundance of combinations of therapeutic modules and carrier function units. DCB-based nanomedicines could be designed to maintain biological friendly synthesis and site-specific release for optimal therapeutic effects, allowing the complex to retain an integrated structure before accumulating at the disease site, but disassembling into individual active components without compromising function in the targeted organs or tissues. In this review, we focus on responsive nanomedicines containing dynamic chemical bonds that can be cleaved by various specific stimuli, enabling achievement of targeted drug release for optimal therapy in various diseases.

BackgroundMesenchymal stem cell paracrine proteins (MSC-PP) offer promising therapeutic potential in the treatment of skin repair. However, there are major challenges of low yield, unknown therapeutic components and insufficient delivery of MSC-PP, thereby limiting the clinic therapeutic application. In the present study, we aimed to establish a three-dimensional dynamic culture system to improve the paracrine activity of UMSC-PP towards the application by modified PEG thermogel to achieve sustained release effect for the treatment of second-grade burn wounds. Methods The 3D dynamic culture was constructed with porous gelatin microcarrier crosslinked with hyaluronic acid (PGM-HA). Umbilical cord mesenchymal stem cells (UMSC) were isolated and cultured in two-dimensional(2D) flasks. P3 UMSC was seeded on PGM-HA at a density of 5,000 cells /cm2 in the 3D dynamic culture system. The culture supernatants were collected and purified by dialysis to collect UMSC-PP. UMSC-PP from 2D and 3D cultures were characterized by mass spectrometric analysis. PEG thermogel was modified from the existing injectable PEG hydrogel. The therapeutic efficacy of mixture of UMSC-PP and PEG was investigated in rabbits second-grade burn models. Results 3D dynamic culture could decrease cell’s apoptosis and improve paracrine proteins effect. And UMSC-PP of 3D dynamic culture system contains proteins that could promote cell proliferation and migration, enhance cell adhesion, and angiogenesis. Moreover, polyethylene glycol (PEG) temperature-sensitive hydrogel was used to carry UMSC-PP to achieve sustained release effect in skin burn rabbit model. Four groups were set to evaluate therapeutic effect in rabbit deep burn skin model: saline control group, PEG group, UMSC-PP group and UMSC-PP+PEG group. The UMSC-PP+PEG group increased the wound healing rate with no abnormal scar compared with the control group. Furthermore, Lgr6+ cell amounts were improved around the wounds in the UMSC-PP+PEG group. In conclusion, combing UMSC-PP from 3D dynamic culture system and PEG hydrogel could produce a sustained and stable therapeutic effect on skin wounds, and regulate the composition and proportion of the extracellular matrix to repair the wound in the direction of normal tissue.Conclusion UMSC-PP from 3D culture contained better therapeutic components, and the mixture of UMSC-PP and PEG thermogel exhibited a powerful healing process by producing a sustained release on the wound and regulating the composition and proportion of the extracellular matrix, decreasing scar formation and wound area.

Shu Luo,1,3 Lina Wang,3 Shuming Li,3 Hongwei Wang,2 Sujing Huang,2 Zhongxia Zhang,2 Ru Wang,2 Hongqiong Guan,3 Yuanhua Huang1,4 1Nanjing Medical University, Nanjing, Jiangsu, 211166, People’s Republic of China; 2Department of Obstetrics, the Second Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570311, People’s Republic of China; 3College of Second Clinical, Hainan Medical University, Haikou, Hainan, 570311, People’s Republic of China; 4Hainan Medical University, Haikou, Hainan, 571199, People’s Republic of ChinaCorrespondence: Yuanhua HuangNanjing Medical University, No. 101, Longmian Avenue, Nanjing, Jiangsu, 211166, People’s Republic of ChinaTel +86-13036095796Email huangyh4180@hainmc.edu.cnHongqiong GuanCollege of Second Clinical, Hainan Medical University, No. 368, Yehai Avenue, Haikou, Hainan, 570311, People’s Republic of ChinaTel +86-13078900121Email guanhq@163.comBackground: Preeclampsia (PE) is an idiopathic hypertensive disorder of pregnancy and is the leading cause of maternal death, fetal malformation, and premature birth. The purpose of this study is to identify the key molecules and lncRNA-related competitive endogenous (ceRNA) regulatory network in PE.Methods: The differentially expressed mRNAs (DEGs), lncRNAs (DELs), and miRNAs (DEMs) were identified between PE and control using the Deseq R package. In addition, we performed Geno ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) on DEGs and DELs-target genes to explore their function. The ceRNA network was established based on lncRNA-miRNA and miRNA-mRNA interactions and visualized using Cytoscape software. LINCO2532, SLCO4A1-AS1, miR23a-5p, and DYNLRB1 were selected for qRT-PCR assay.Results: Using microarray analysis, we screened 726 DELs (456 upregulated and 370 downregulated), 49 DEMs (37 upregulated and 12 downregulated), and 318 DEGs (230 upregulated and 88 downregulated) between PE patients and control. Based on lncRNA-miRNA pairs and miRNA-mRNA pairs, the ceRNA network was constructed, which contained 16 lncRNA, 1 miRNA (miR-23a-5p), and 1 mRNA (DYNLRB1). LncRNA (LINCO2532 and SLCO4A1-AS1) and DYNLRB1 were downregulated and the expression of miR23a-5p was upregulated in PE patients compared with healthy controls.Conclusion: In this study, the novel ceRNA network was established in the placentas of PE patients. It elucidated the regulatory mechanism of PE, and identified novel PE biomarkers, which have important guiding significance for clinical treatment and further scientific research of PE.Keywords: preeclampsia, lncRNA, miRNA, ceRNA network

Tissue engineering cornea has shown great clinical potential for cornea reconstruction, but efficient recovery of natural structure and physiological function remains great challenges. In this study, the acellular porcine corneal stroma (APCS) was prepared by a phospholipase A

Since the global financial crisis, non-reserve-issuing economies (NREs) have been highly sensitive to episodes of external pressures. With monetary policy independence constrained by this sensitivity, many NREs have utilized other policy instruments. This paper confirms the vulnerability of NREs to external shocks and finds that in some circumstances managing such shocks with multiple instruments can both lessen the policy response required from any one policy tool to financial and external shocks and increase the effectiveness of policies in stabilizing macro-financial conditions. Effectiveness however does not always imply appropriateness, which rests on an evaluation of potential trade-offs and unintended consequences.

In this paper, we propose a model-free unsupervised learning approach to forecast real-time locational marginal prices (RTLMPs) in wholesale electricity markets. By organizing system-wide hourly RTLMP data into a 3-dimensional (3D) tensor consisting of a series of time-indexed matrices, we formulate the RTLMP forecasting problem as a problem of generating the next matrix with forecasted RTLMPs given the historical RTLMP tensor, and propose a generative adversarial network (GAN) model to forecast RTLMPs. The proposed formulation preserves the spatio-temporal correlations among system-wide RTLMPs in the format of historical RTLMP tensor. The proposed GAN model learns the spatio-temporal correlations using the historical RTLMP tensors and generate RTLMPs that are statistically similar and temporally coherent to the historical RTLMP tensor. The proposed approach forecasts system-wide RTLMPs using only publicly available historical price data, without involving confidential information of system model, such as system parameters, topology, or operating conditions. The effectiveness of the proposed approach is verified through case studies using historical RTLMP data in Southwest Power Pool (SPP).

Ion mobility spectrometry (IMS) which acts as a rapid analysis technique is widely used in the field detection of illicit drugs and explosives. Due to limited separation abilities of the pint-sized IMS challenges and problems still exist regarding high false positive and false negative responses due to the interference of the matrix. In addition, the gas-phase ion chemistry and special phenomena in the IMS spectra, such one substance showing two peaks, were not identified unambiguously. In order to explain or resolve these questions, in this paper, an ion mobility spectrometry was coupled to a mass spectrometry (IMS-MS). A commercial IMS is embedded in a custom-built ion chamber shell was attached to the mass spectrometer. The faraday plate of IMS was fabricated with a hole for the ions to passing through to the mass spectrometer. The ion transmission efficiency of IMS-MS was optimized by optimizing the various parameters, especially the distance between the faraday plate and the cone of mass spectrum. This design keeps the integrity of the two original instruments and the mass spectrometry still works with multimode ionization source (i.e., IMS-MS, ESI-MS, APCI-MS modes). The illicit drugs and explosive samples were analyzed by the IMS-MS with 63Ni source. The results showed that the IMS-MS is of high sensitivity. The ionization mechanism of the illicit drug and explosive samples with 63Ni source were systematically studied. In addition, the interferent which interfered the detection of cocaine was identified as dibutyl phthalate (DBP) by this platform. The reason why the acetone solution of amphetamine showed two peaks was explained.

Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010). This paper implements statistical training for large-scale Chinese corpus through neural network language model and proposes an automatic method of constructing a multidimensional sentiment lexicon based on constraints of coordinate offset. In order to distinguish the sentiment polarities of those words which may express either positive or negative meanings in different contexts, we further present a sentiment disambiguation algorithm to increase the flexibility of our lexicon. Lastly, we present a global optimization framework that provides a unified way to combine several human-annotated resources for learning our 10-dimensional sentiment lexicon SentiRuc. Experiments show the superior performance of SentiRuc lexicon in category labeling test, intensity labeling test, and sentiment classification tasks. It is worth mentioning that, in intensity label test, SentiRuc outperforms the second place by 21 percent.

The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.

This paper studies growth patterns in Emerging Market Economies (EMs) from the perspective on clusters and taxonomies. First, it documents developments over the past five decades in EMs and uses a cluster analysis to better understand convergence and the investment-growth nexus. Second, it looks at the performance of EMs since 2000 and develops a taxonomy to classify countries according to their factor endowments as well as their real and financial external linkages. The taxonomy offers insights on growth dynamics pre and post the global financial crisis. Results highlight the high degree of heterogeneity in EMs and the need for more granular and targeted near and long-term policy advice.

In membrane separation processes, the presence of the concentration polarization boundary layer (CPBL) often leads to a decline in the permeate flux and an increased risk of membrane fouling. Although the study of membrane fouling has benefited from the recent development of real-time, non-invasive acoustic measurements of the fouling process under realistic conditions, measurement of concentration polarization is still difficult and rather limited. This paper describes the development of interdigitated capacitive microsensors for real-time in-situ monitoring of CPBL growth in a cross-flow nanofiltration membrane module using calcium sulfate (CaSO 4 ) solution as the feed. The developed capacitive sensors respond to changes in solute concentration as a function of distance from the membrane surface. Detection capability was determined in a series of comprehensive experiments with systematic changes in feed concentration and module operating parameters including upstream pressure, flow rate, and feed composition. The specially designed microscale capacitor assembly provides a unique capability for real-time characterization of the transient and steady-state concentration boundary layer polarization behavior. The capacitive microsensor concentration polarization measurements were corroborated via independent membrane morphological analysis and standard performance metrics. The experimental data were in excellent qualitative agreement with theoretical predictions from a mathematical model of concentration polarization behavior. In addition, the use of microcapacitor sensors in combination with ultrasonic time-domain reflectometry, enabled real-time observation of the development of scaling from the initiation of concentration polarization through the onset of membrane fouling. These results clearly demonstrate the advantages of a multisensor approach to the measurement of membrane phenomena, and provide a basis for the development of “smart” membranes.

The power consumption and complexity of closed-loop control electronics are the major barriers that limit the applications of optical switches, beam steering devices and other micromirrors. To eliminate or lower these barriers, it is desirable to have a micromirror with precise, multi-level digitally positioned angles. This paper reports the investigation to design multi-level digitally positioned micromirrors and characterize their precision levels. From the experimental data, very encouraging results have been reported for the first designed multi-level digital micromirror: ±0.01° position precision has been achieved with the mirrors fabricated in the same batch. In order to reduce the number of the electrical drives for the mirror and increase the maximum tilting angle, an improved device is designed and tested. The precision of 0.034°/V has been demonstrated in the digital levels via experimental testing and ±0.03° position precision of repeatability has been achieved within the angle range ±3.5°. Standard deviation of average angle and angle variance per driving voltage within the digital levels are identified to characterize the digital behavior of the mirror. The electro-mechanical performance of the mirror is discussed. Analytical and full coupled-domain finite elements/boundary elements (FEM/BEM) models verified by experiments are developed to predict and simulate the electro-mechanical behavior of the micromirror and are used to guide the mirror’s design for the desired digital performance.

After a short-lived slowdown in the immediate aftermath of the global financial crisis and a swift rebound, emerging markets (EM) are now entering a period of slower growth. In fact, growth is now lower than the post-crisis peak of 2010-11, as well as the rates seen in the decade before the crisis. This raises the question of whether EMs can bounce back to the growth rates seen in the last decade or whether their prospects are dimmer than thought a few years ago. This SDN we will explore the drivers of the slowdown, how changes in external conditions that supported high growth in EMs will affect them over the medium term, and the policy priorities needed to sustain the growth rates seen in the past decades. In doing so, the paper differentiates EMs along various dimensions (e.g. degree of commodity dependence, trade and financial openness) to highlight the need to tailor policy priorities.

In this study, 6-hydroxydopamine was stereotaxically injected into the right substantia nigra compact and ventral tegmental area of rats to establish Parkinson's disease models. The rats then received a transplantation of bone marrow stromal cells that were previously isolated, cultured and labeled with 5-bromo-2’-deoxyuridine in vitro. Transplantation of the bone marrow stromal cells significantly decreased apomorphine-induced rotation time and the escape latency in the Morris water maze test as compared with rats with untreated Parkinson's disease. Immunohistochemical staining showed that, 5-bromo-2’-deoxyuridine-immunoreactive cells were present in the lateral ventricular wall and the choroid plexus 1 day after transplantation. These immunoreactive cells migrated to the surrounding areas of the lateral cerebral ventricle along the corpus callosum. The results indicated that bone marrow stromal cells could migrate to tissues surround the cerebral ventricle via the cerebrospinal fluid circulation and fuse with cells in the brain, thus altering the phenotype of cells or forming neuron-like cells or astrocytes capable of expressing neuron-specific proteins. Taken together, the present findings indicate that bone marrow stromal cells transplanted intracerebroventricularly could survive, migrate and significantly improve the rotational behavior and cognitive function of rats with experimentally induced Parkinson's disease.

In membrane separation processes, the presence of the concentration polarization boundary layer (CPBL) can lead to a decline in the flux and an increased risk of membrane fouling. This paper describes the development of capacitive microsensors for real-time in-situ monitoring of CPBL growth in a nanofiltration membrane module using CaSO/sub 4/ solution as the feed. The sensors respond to changes in solute concentration as a function of distance from the membrane surface. Detection capability was determined in a series of comprehensive experiments with systematic changes in feed concentration and module operating parameters. The specially designed microcapacitor assembly provides a unique capability for real-time characterization of the transient and steady state concentration polarization behavior.

A novel method based on hollow fibre liquid phase microextraction coupled with a hand-held ion mobility spectrometer was developed for determination of seven pesticides in cucumber samples. The hollow fibre liquid phase microextraction (LPME) sample preparation method is used to preconcentrate pesticide residues present in cucumber samples. The extracted pesticide residues are detected by IMS under optimized operating conditions. During the tests, the instrument is operated in the positive mode at ambient pressure using 63Ni as the ionization source, with dry, clean air as the drift gas which circulates in a closed loop system. The IMS detection limits for propoxur, carbofuran, atrazine, cyanatryn, metolachlor, prometryn and tebuconazole were 5, 5, 1, 5, 5, 10 and 50 ng respectively. The analysis via IMS takes less than 10 s. The mean recoveries were in the range of 60–89% with an acceptable reproducibility (RSD < 12%). The results obtained reveal that the method is of great potentiality for rapid screening and detection of seven pesticide residues contained in agricultural products.