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In power systems, failures of vulnerable lines can trigger large-scale cascading failures, and vulnerability assessment is dedicated to locating these lines and reducing the risks of such failures. Based on a structure and attribute network embedding (SANE) algorithm, a novel quantitative vulnerability analysis method is proposed to identify vulnerable lines in this research. First, a two-layered random walk network with topological and electrical properties of transmission lines is established. Subsequently, based on the weighted degree of nodes in the two-layered network, the inter-layer and intra-layer walking transition probabilities are developed to obtain walk sequences. Then, a Word2Vec algorithm is applied to obtain low-dimension vectors representing transmission lines, according to obtained walk sequences for calculating the vulnerability index of transmissions lines. Finally, the proposed method is compared with three widely used methods in two test systems. Results show the network embedding based method is superior to those comparison methods and can provide guidance for identifying vulnerable lines.

The accuracy of photovoltaic (PV) power forecasting techniques relies not only on high-quality spatiotemporal data but also on an efficient feature-mining methodology. In this study, a spatiotemporal power forecasting model based on the broad learning system (BLS) and the improved backtracking search optimization algorithm (IBSOA) is proposed. The objective is to enhance the accuracy of PV power predictions while reducing the time-intensive training process associated with an extensive set of broad learning system parameters. The spatiotemporal attributes of historical data from multiple PV sites are clustered using a self-organizing map. The clustering analysis explores the spatiotemporal correlation among five photovoltaic (PV) power stations for each season between 2017 and 2018. Subsequently, the IBSOA is employed to optimize the hyperparameters of the BLS model, particularly the mapping and enhancement nodes. By utilizing hyperparameter optimization, a BSOA-based broad learning model is introduced to achieve superior accuracy. The results are assessed using the proposed method in comparison with three popular optimization algorithms: 1) genetic algorithm (GA), 2) bird swarm algorithm (BSA), and 3) backtracking search optimization algorithm (BSOA). All scenarios are validated and compared using PV plant data from the DKA center in Australia. The root-mean-square error (RMSE) indicators of the proposed prediction method are consistently lower than the worst-case scenario in each season, decreasing by 3.2283 kW in spring, 3.9159 kW in summer, 1.3425 kW in autumn, and 1.4058 kW in winter. Similarly, the mean absolute percentage error (MAPE) exhibits a reduction compared to the worst case, with a decreases of 0.882% in spring, 1.2399% in summer, 1.803% in autumn, and 1.087% in winter. The comprehensive results affirm that the proposed method surpasses alternative optimization techniques, delivering high-quality power forecasts for the given case study.

Abstract A novel optimal allocation framework for photovoltaic generations in an integration system of buildings‐to‐distribution‐network using improved backtracking search optimization algorithm is proposed here. In the proposed framework, photovoltaic generations are optimally allocated to optimize the overall performance of a buildings‐to‐distribution‐network regarding the efficient active power usage of photovoltaic generations, the energy savings, and voltage profile improvement of distribution network. The effects of building active demand response on the photovoltaic generations' optimal allocation are considered in the proposed framework. An improved backtracking search algorithm using two new operators is developed to optimize the active power reduction factors and locations of photovoltaic generations. The test results of IEEE 33‐bus and 69‐bus systems demonstrate that the developed framework can take full advantage of photovoltaic generation power and the active demand response of buildings to coordinate the efficient active power usage of photovoltaic generations, the voltage profile improvement and energy savings of a buildings‐to‐distribution‐network. In addition, the improved backtracking search optimization algorithm converges faster than genetic algorithm, classical backtracking search optimization algorithm, and bird swarm algorithm.

BackgroundPicosecond lasers are widely used in dermatologic and cosmetic practice. In clinical practice, informed consent for laser treatments is critical to ensure patients’ understanding of health information.ObjectivesTo evaluate whether video-based informed consent improves patient comprehension and satisfaction.MethodsThe study was performed from August 1 to November 30, 2022. Solar lentigines patients who fulfilled the inclusion criteria were included. Before October 1, 2022, traditional informed consent methods were performed. In the subsequent 2 months, a video-based informed consent was used as an adjunct to traditional consenting methods. Finally, patient comprehension of relevant knowledge about laser treatment and client satisfaction were assessed.ResultsA total of 106 patients were included. The mean number of correct answers in the comprehension assessment in the video-based informed consent group was significantly higher than that in the traditional informed consent group (4.4 ± 1.2 vs. 3.4 ± 1.1, p < 0.001). Compared to the traditional informed consent group, more correct answers in the video-based informed consent group were provided by older patients (3.9 ± 1.2 vs. 2.9 ± 1.1, p = 0.004) and patients with lower education levels (4.1 ± 1.1 vs. 3.0 ± 1.2, p < 0.001). The mean satisfaction score in the video-based informed consent group was significantly higher than that in the traditional informed consent (27.8 ± 5.7 vs. 24.3 ± 6.2, p = 0.003).ConclusionVideo-based informed consent helps patients learn clinical literacy more effectively and improves patient satisfaction, especially in those with lower education levels and older ages.

Weathered coal is a type of coal with barren utility value, which is exposed on the surface for a prolonged time or shallowly buried in coal seams, and the physicochemical properties of weathered coal have changed compared with high‐quality coal. China is also a country with large reserves of weathered coal, therefore, the value utilization of weathered coal has received tremendous scientific attention. Herein, the main purpose is to explore the combined effect of surfactants and microorganisms on bioconversion of weathered coal to produce humic substances. Tween‐80, Phanaerochaete chrysosporium (J1) and Trametes versicolor (J2) were selected as surfactants and engineering strains to convert weathered coal. 3D fluorescence spectra were used to analyze the species and contents of humic acids during the biosolubilization process of weathered coal. Results revealed that both J1 and J2 exhibited good performance on weathered coal biosolubilization (increasing 10% and 14% compared to the control, respectively). The intensity of weathered coal biosolubilization was significantly increased by 15% and 12% when J1 and J2 were applied in combination with Tween‐80. And the addition of Tween‐80 also distinctly reduced the molecular weight of humic substances. In summary, surfactant plays an important role in bioconversion of weathered coal, while molecular weight of humic substances was different that weathered coal biosolubilization under the co‐action of J1 and Tween‐80 increased and J2 with Tween‐80 was opposite. In short, this project might provide a new strategy for exploring the biodegradation of weathered coal. [ABSTRACT FROM AUTHOR]

Abstract Background Brain metastasis (BM) is thought to be related to the mortality and poor prognosis of non-small cell lung cancer (NSCLC). Despite promising development of NSCLC treatment, the treatment of NSCLC BM is still not optimistic due to the existence of the blood-brain barrier (BBB) that prevent drug penetration, as well as the short median survival time of the patients left for treatment. In this context, further development of quick and effective pre-clinical models is needed in NSCLC BM treatment. Here, we report a model system using zebrafish to promote the development of drugs for patients with NSCLC BM. Methods Three different NSCLC cell lines (H1975, A549 and H1299) were used to establish zebrafish BM models. The embryo age and cell number for injection were first optimized. Metastatic cells were observed in the brain blood vessels of zebrafish and were verified by hematoxylin-eosin (HE) staining. Then, the metastasis potentials of H1975 and A549 with manipulated microRNA-330-3p (miR-330-3p) expression were also investigated. Finally, sensitivities of H1975 and A549 to osimertinib and gefitinib were tested. Results This zebrafish BM model could distinguish NSCLC cell lines with different BM potential. Over-expressed miR-330-p significantly improved the BM potential of the A549 cells while knockdown miR-330-p reduced the BM ability of the H1975 cells. Both osimertinib and gefitinib showed inhibition effect in zebrafish BM model with the inhibition rate higher than 50 %. H1975 cell showed much higher sensitivity to osimertinib rather than gefitinib both in vivo and in vitro. Conclusions We established zebrafish brain metastasis model for studying mechanism and treatment of NSCLC BM. This study provided a useful model for NSCLC brain metastasis that could be used to study the mechanism that drive NSCLC cells to the brain as well as identify potential therapeutic options.

Introduction: Despite proven benefits, patients with coronary heart disease (CHD) typically fail to participate in sufficient physical activity (PA). Effective interventions should be implemented to help patients maintain a healthy lifestyle and modify their present behavior. Gamification is the use of game design features (such as points, leaderboards, and progress bars) to improve motivation and engagement. It shows the potential for encouraging patients to engage in PA. However, empirical evidence on the efficacy of such interventions among patients with CHD is still emerging. Purpose: The aim of the study is to explore whether a smartphone-based gamification intervention could increase PA participation and other physical and psychological outcomes in CHD patients. Methods: Participants with CHD were randomly assigned to three groups (control group, individual group, and team group). The individual and team groups received gamified behavior intervention based on behavioral economics. The team group combined gamified intervention with social interaction. The intervention lasted for 12 weeks, and the follow-up was12 weeks. The primary outcomes included the change in daily steps and the proportion of patient days that step goals were achieved. The secondary outcomes included competence, autonomy, relatedness, and autonomous motivation. Results: For the individual group, smartphone-based gamification intervention significantly increased PA among CHD patients over the 12-week period (step count difference 988; 95% CI 259−1717; p < 0.01) and had a good maintenance effect during the follow-up period (step count difference 819; 95% CI 24−1613; p < 0.01). There are also significant differences in competence, autonomous motivation, body mass index (BMI), and waist circumference in 12 weeks between the control group and individual group. For the team group, gamification intervention with collaboration didn't result in significant increases in PA. But patients in this group had a significant increase in competence, relatedness, and autonomous motivation. Conclusion: A smartphone-based gamification intervention was proven to be an effective way to increase motivation and PA engagement, with a substantial maintenance impact (Chinese Clinical Trial Registry Identifier: ChiCTR2100044879). [ABSTRACT FROM AUTHOR]

Purpose: To reveal molecular mechanisms of diabetic retinopathy (DR) in Asians and facilitate the identification of new therapeutic targets through untargeted metabolomics. To determine the differences in serum metabolites and metabolic pathways between different stages of diabetic retinopathy in patients with type 2 diabetic mellitus (T2DM) and proliferative DR (PDR) and non-proliferative DR (NPDR) and identify differential metabolites between T2DM and DR (NPDR and PDR) patients.Methods: This prospective observational registration study described the differential metabolites between 45 T2DM patients and 15 control cases with no significant differences in clinical characteristics. Their biospecimens and clinical information were collected and recorded in their medical reports. DR phenotypes of the subjects were verified by retina specialists. Serum metabolites were analyzed using high-resolution mass spectrometry with liquid chromatography. Untargeted metabolomics was performed on serum samples from 15 T2DM patients, 15 non-proliferative diabetic retinopathy patients, 15 proliferative diabetic retinopathy patients, and 15 diabetic controls. Discriminatory metabolic features were identified through partial least squares discriminant analysis (PLS-DA), hierarchical clustering analysis (HCA), and generalized linear regression models.Result: Through untargeted metabolomics, 931 features (523 in positive and 408 in negative modes) with 102 common metabolites highly relevant to the presence of DR were detected. In the adjusted analysis, 67 metabolic features differed significantly between T2DM and NPDR patients. Pathway analysis revealed alterations in metabolisms of amino acids and fatty acids. Glutamate, phosphatidylcholine, and 13-hydroperoxyoctadeca-9,11-dienoic acid (13-PHODE) were key contributors to these pathway differences. A total of 171 features distinguished PDR patients from T2DM patients, and pathway analysis revealed alterations in amino acid metabolism, fatty acid metabolism, nitrogen metabolism, and tricarboxylic acid cycle. Aspartate, glutamate, glutamine, ornithine, N-acetyl-l-glutamate, N-acetyl-l-aspartate, citrate, succinate, N-(L-arginino)succinate, 2-oxoglutarate, 13-hydroperoxyoctadeca-9,11-dienoic acid, methionine, lysine, threonine, phenylalanine, N(pi)-methyl-l-histidine, phosphatidylcholine, and linoleate were major contributors to the pathway differences. Between NPDR patients and PDR patients, there were 79 significant differential metabolites. Enrichment pathway analysis showed changes in amino acid metabolism, fatty acid metabolism, pantothenate, and CoA biosynthesis. Aspartate, glutamine, N-acetyl-l-glutamate, N-acetyl-l-aspartate, pantothenate, dihomo-gamma-linolenate, docosahexaenoic acid, and icosapentaenoic acid were key factors for the differences of these pathways.Conclusion: This study demonstrated that the pathways of arginine biosynthesis metabolism, linoleic acid metabolism, alanine, aspartate, and glutamate metabolism, as well as d-glutamine and d-glutamate metabolism, were dysregulated in DR patients of the Asian population. Increased levels of glutamate, aspartate, glutamine, N-acetyl-l-glutamate, and N-acetyl-l-aspartate and decreased levels of dihomo-gamma-linolenate, docosahexaenoic, and icosapentaenoic were considered as the metabolic profile that could distinguish PDR from NPDR in Asians. Phosphatidylcholine and 13-PHODE were identified as two major novel metabolite markers in advanced stages of DR in our study.

AIM: To evaluate the two-year outcomes of patients treated for diabetic macular edema (DME) with intravitreal Conbercept (IVC). METHODS: The clinical data of 30 DME patients (36 eyes) were retrospectively reviewed. The patients were treated with IVC for 3mo. Additional IVC was given at subsequent monthly visits, if needed (3+PRN). The patients were followed up for 24mo. RESULTS: The best-corrected visual acuity (BCVA) at 24mo significantly increased (66.7±15.3 letters) in comparison with the baseline (54.4±15.4 letters, P

Ciphertext-policy attribute-based encryption (CP-ABE) is very suitable for data access control in cloud environment. The existing CP-ABE algorithm does not consider that the access structure of multiple files has a hierarchical relationship, and it needs to encrypt each file to realize its access control requirements, which leads to large costs. In addition, most schemes only have a single authorized institution to manage the key, which has high requirements on the computing power and honesty of the authorized institution. This paper proposes a cloud data access control scheme based on blockchain with multi-authority (BMAC). In BMAC, this paper designs a hierarchical CP-ABE algorithm, for multiple data files with hierarchical access structure, one encryption only, then visitors can decrypt part of the files when meeting some access conditions and get all files when meeting all conditions. This paper also designs a multi-authority key management method based on blockchain, which enables all authorized institutions to distribute private keys honestly and concurrently through blockchain technology. Performance and security analysis show that BMAC can effectively protect data confidentiality, resist collusion attack, achieve secure and efficient fine-grained data access control and decentralized private key distribution.

Elastolytic giant cell granuloma, an idiopathic granulomatous dermatosis, is characterized by annular plaques on sun-exposed areas, and has been termed actinic granuloma or annular elastolytic giant cell granuloma. Many atypical clinical manifestations and lesions involving sun-protected areas have been reported. The aims of this retrospective study of 105 patients were to summarize the clinical and histological features of patients with this condition; to provide evidence for the viewpoint that elastolytic giant cell granuloma is a better term to include all clinical morphological types presenting with elastolysis, elastophagocytosis, and an infiltrate of multinucleated giant cells histologically; and to establish a new clinical classification. The varying clinical manifestations were further categorized into annular, papular, giant, mixed and generalized forms. The pathological manifestations were classified into giant cell, necrobiotic, histiocytic, sarcoidal and mixed patterns. Diabetes mellitus or impaired glucose tolerance were the most commonly identified comorbidities. Oral low-dose corticosteroid may be an effective treatment.

Vitiligo is a common acquired skin disorder caused by immune-mediated destruction of epidermal melanocytes. Systemic glucocorticoids (GCs) have been used to prevent the progression of active vitiligo, with 8.2–56.2% of patients insensitive to this therapy. Currently, there is a lack of biomarkers that can accurately predict and evaluate treatment responses. The goal of this study was to identify candidate urinary protein biomarkers to predict the efficacy of GCs treatment in active vitiligo patients and monitor the disease. Fifty-eight non-segmental vitiligo patients were enrolled, and 116 urine samples were collected before and after GCs treatment. Patients were classified into a treatment-effective group (n = 42) and a treatment-resistant group (n = 16). Each group was divided equally into age- and sex-matched experimental and validation groups, and proteomic analyses were performed. Differentially expressed proteins were identified, and Ingenuity Pathway Analysis was conducted for the functional annotation of these proteins. Receiver operating characteristic curves were used to evaluate the diagnostic value. A total of 245 and 341 differentially expressed proteins between the treatment-resistant and treatment-effective groups were found before and after GCs treatment, respectively. Bioinformatic analysis revealed that the urinary proteome reflected the efficacy of GCs in active vitiligo patients. Eighty and fifty-four candidate biomarkers for treatment response prediction and treatment response evaluation were validated, respectively. By ELISA analysis, retinol binding protein-1 and torsin 1A interacting protein 1 were validated to have the potential to predict the efficacy of GCs with AUC value of 1 and 0.875, respectively. Retinol binding protein-1, torsin 1A interacting protein 1 and protein disulfide-isomerase A4 were validated to have the potential to reflect positive treatment effect to GCs treatment in active vitiligo with AUC value of 0.861, 1 and 0.868, respectively. This report is the first to identify urine biomarkers for GCs treatment efficacy prediction in vitiligo patients. These findings might contribute to the application of GCs in treating active vitiligo patients.

Persistent eruption occurs in a subset of patients with adult-onset Still’s disease. In our experience, a considerable proportion of these patients present with peripheral eosinophilia. The aims of this study were to summarize the clinical and histological features of patients with adult-onset Still’s disease with persistent eruption in the current study cohort, and to assess the association between peripheral eosinophil levels and disease characteristics. A total of 21 patients with adult-onset Still’s disease with persistent eruption were included in this retrospective study. Koebner signs, an important diagnostic clue, were found in 85.7% of patients. The proportion of patients presenting with eosinophilia within the disease course was 57.1%. Skin histology revealed infiltration of eosinophils in 90.5% of patients. Peripheral eosinophil levels were positively associated with involved body surface area. Patients with normal peripheral eosinophil counts were more likely to achieve complete remission than those with abnormal peripheral eosinophil counts. Eosinophils may be involved in the pathogenesis of skin eruption. Abnormal peripheral eosinophil counts in these patients may indicate a more refractory disease course.