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With the development of positioning technology and the widespread application of mobile positioning terminal devices, the acquisition of trajectory data has become increasingly convenient. Furthermore, mining information related to scenic spots and tourists from trajectory data has also become increasingly convenient. This study used the normalization results of information entropy to evaluate the attraction of scenic spots and the experience index of tourists. Tourists and scenic spots were chosen as the probability variables to calculate information entropy, and the probability values of each variable were calculated according to certain methods. There is a certain competitive relationship between scenic spots of the same type. When the distance between various scenic spots is relatively close (less than 8 km), a strong cooperative relationship can be established. Scenic spots with various levels of attraction can generally be classified as follows: cultural heritage, natural landscape, and leisure and entertainment. Scenic spots with higher attraction are usually those with a higher A-level and convenient transportation. A considerable number of tourists do not choose to visit crowded scenic destinations but choose some spots that they are more interested in according to personal preferences and based on access to free travel.

Dynamic contingency analysis (DCA) for modern power systems is fundamental to help researchers and operators look ahead of potential issues, arrange operational plans, and improve system stabilities. However, since the system size and the number of contingency scenarios continue to increase, pursuing more effective computational performance faces many difficulties, such as slow algorithms and limited computing resources. This research accelerates the intensive computations of massive DCAs by implementing a two-level hierarchical computing architecture on graphical processing units (GPUs). The performance of the designed method is examined using four test systems of different sizes and compared with a CPU-based parallel approach. The results show up to 2.8x speedup using one GPU and 4.2x speedup using two GPUs, respectively. More accelerations can be observed once more GPUs are configured. It demonstrates that the proposed architecture can significantly enhance the overall computational performance of massive DCAs while maintaining a strong scaling capability under various resource configurations.

Abstract The doubly fed induction generator (DFIG) usually experiences high rotor current and DC capacitor link voltage spikes during system fault events. In this paper, a novel data‐driven approach is proposed to enhance DFIG performance under fault scenarios. An advanced reinforcement learning algorithm called guided surrogate‐gradient‐based evolution strategy (GSES) is used to control the DFIG power and capacitor DC‐link voltage by adjusting the optimal reference signals. This controller is able to prevent the DFIG rotor from over‐current risk and maintain grid‐connected operation. The proposed GSES‐based control algorithm was evaluated through simulations on a 3.6‐MW DFIG in the PSCAD/EMTDC software. Results have validated the effectiveness of the proposed GSES‐based control algorithm in improving DFIG performance under various fault scenarios.

Abstract Inter‐area oscillation is a serious problem that threatens a power system. Appropriate damping control of the inter‐area oscillation would ensure the grid stability and maintain the tie‐line power transfer capability. In this paper, a novel reinforcement learning (RL) based power oscillation damping (POD) controller is proposed that uses Thyristor Controlled Series Compensators (TCSC) to damp inter‐area oscillations. By leveraging the unbiased gradient direction estimation of the natural evolution strategy (NES), the power flows on the tie‐lines were successfully regulated and inter‐area oscillations were damped through dynamically modulating the inserted reactance of the TCSC. Furthermore, parallel computation techniques were adopted to speed up the training process of the NES. The proposed RL‐based POD controller has been tested on both two‐area four‐machine system and North American MinniWECC system. Extensive studies have demonstrated the excellent performance of the proposed RL‐based TCSC POD controller in damping inter‐area oscillations.

Abstract Background Heterogeneity of leukemia‐initiating cells (LICs) is a major obstacle in acute myeloid leukemia (AML) therapy. Accumulated evidence indicates that the coexistence of multiple types of LICs with different pathogenicity in the same individual is a common feature in AML. However, the functional heterogeneity including the drug response of coexistent LICs remains unclear. Therefore, this study aimed to clarify the intra‐heterogeneity in LICs that can help predict leukemia behavior and develop more effective treatments. Methods Spleen cells from the primary Setd2−/−‐AML mouse were transplanted into C57BL/6 recipient mice to generate a transplantable model. Flow cytometry was used to analyze the immunophenotype of the leukemic mice. Whole‐genome sequencing was conducted to detect secondary hits responsible for leukemia transformation. A serial transplantation assay was used to determine the self‐renewal potential of Setd2−/−‐AML cells. A limiting‐dilution assay was performed to identify the LIC frequency in different subsets of leukemia cells. Bulk and single‐cell RNA sequencing were performed to analyze the transcriptional heterogeneity of LICs. Small molecular inhibitor screening and in vivo drug treatment were employed to clarify the difference in drug response between the different subsets of LICs. Results In this study, we observed an aged Setd2−/− mouse developing AML with co‐mutation of NrasG12S and BrafK520E. Further investigation identified two types of LICs residing in the c‐Kit+B220+Mac‐1− and c‐Kit+B220+Mac‐1+ subsets, respectively. In vivo transplantation assay disclosed the heterogeneity in differentiation between the coexistent LICs. Besides, an intrinsic doxorubicin‐resistant transcriptional signature was uncovered in c‐Kit+B220+Mac‐1+ cells. Indeed, doxorubicin plus cytarabine (DA), the standard chemotherapeutic regimen used in AML treatment, could specifically kill c‐Kit+B220+Mac‐1− cells, but it hardly affected c‐Kit+B220+Mac‐1+ cells. Transcriptome analysis unveiled a higher activation of RAS downstream signaling pathways in c‐Kit+B220+Mac‐1+ cells than in c‐Kit+B220+Mac‐1− cells. Combined treatment with DA and RAS pathway inhibitors killed both c‐Kit+B220+Mac‐1− and c‐Kit+B220+Mac‐1+ cells and attenuated disease progression. Conclusions This study identified two cell subsets enriched for LICs in murine Setd2−/−‐AML and disclosed the transcriptional and functional heterogeneity of LICs, revealing that the coexistence of different types of LICs in this model brings about diverse drug response.

Background: Qingfei Paidu decoction (QFPDD) has been widely used in treating coronavirus disease 2019 (COVID-19) in China. However, studies on the treatment effect of COVID-19 patients and other respiratory diseases have not been well demonstrated. Our study aims to determine the treatment effect of QFPDD in combination with conventional treatment on COVID-19 patients and other respiratory diseases.Methods: This retrospective study recruited COVID-19 patients who were treated with QFPDD for at least two courses (6 days) from seven hospitals in five provinces from January 21 to March 18 2020. Demographic, epidemiological, clinical, laboratory, computed tomography characteristics, treatment, and outcome data were collected and analyzed. The improvements in clinical symptoms before and after QFPDD treatment were compared.Results: Eight COVID-19 patients were included in this study. Of them, six were males (75.0%). The median age of the patients was 66 (60–82) years. Four patients were classified as mild and moderate cases (50.0%); there were two severe cases (25.0%) and critical cases (25.0%). The most common symptom was cough (7 [87.5%]), followed by fever (6 [75.0%]), fatigue (4 [50.0%]), asthma (4 [50.0%]), and anorexia (3 [37.5%]). Abnormal findings included decrease in neutrophils (3 [37.5%]), lymphocytes (2 [25.0%]), alkaline phosphatase (3 [37.5%]), lactic dehydrogenase (4 [50.0%]), erythrocyte sedimentation rate (2 [25.0%]), and C-reactive protein (5 [83.3%]) at admission. After one course (3 days) of QFPDD, nasal obstruction and sore throat completely disappeared, and fever (5 [83.3%]), fatigue (2 [50.0%]), and cough (2 [28.6%]) were improved. After two courses (6 days), the fever disappeared completely in all patients, and the other symptoms showed a tendency to improve. In non-severe patients, 87.5% baseline symptoms completely disappeared. In severe patients, 61.1% of the baseline symptoms completely disappeared after patients were administered QFPDD for two courses. Of the abnormal indicators, 55.6% returned to normal levels. The median duration to complete fever recovery was 1.0 day. The median durations of viral shedding and hospitalization were 10.5 and 21.5 days, respectively. None of the patients worsened and died, and no serious adverse events occurred related to QFPDD during hospitalization.Conclusion: QFPDD combined with conventional treatment improved clinical symptoms in COVID-19 patients with other respiratory diseases, and no serious adverse reactions associated with QFPDD were observed. Larger sample studies confirm our findings in the future.

With increasing penetration of renewable energy and active market participation, power system operation scenarios and patterns have increased exponentially. This has led to challenges in identifying a good subset of scenarios for routine planning, operation, and emerging machine learning applications. To address these challenges, we develop an approach integrating comprehensive exploratory data analyses and smart sampling techniques to identify and select a small subset of representative power system scenarios that maintain the coverage of system scenarios and operation envelope, therefore, leading to very efficient, yet representative studies and analysis. We propose a hierarchical Latin Hypercube Sampling (LHS) technique for smart sampling, which allows free-form distributions of system load and considers generator commitment status along with generation levels. A set of performance metrics are also defined for systematic evaluation of the adequacy and efficiency of the sampled cases. The developed approach and metrics are demonstrated using the Texas 2000 bus system in this paper and will be extended to the more complex real world systems such as Western Interconnect System.

Background. Coronavirus disease 2019 (COVID-19) has been a global threat that pushes healthcare to its limits. Hypertension is one of the most common risk factors for cardiovascular complications in COVID-19 and is strongly associated with disease severity and mortality. To date, clinical mechanisms by which hypertension leads to increased risk in COVID-19 are still unclear. Furthermore, additional factors might increase these risks, such as the consideration of age and sex, which are of interest when in search of personalized treatments for hypertensive COVID-19 patients. Methods. We conducted a retrospective cohort study of 543 COVID-19 patients in seven provinces of China to examine the epidemiological and clinical characteristics of COVID-19 in this population and to determine risk factors of hypertensive COVID-19 patients. We also used univariable and multivariable logistic regression methods to explore the risk factors associated with hypertensive COVID-19 patients in different age and sex subgroups. Results. Among the enrolled COVID-19 patients, the median age was 47 years (interquartile range (IQR) 34.0–57.0), and 99 patients (18.23%) were over 60 years old. With regard to comorbidities, 91 patients (16.75%) were diagnosed with hypertension, followed by diabetes, coronary disease, and cerebrovascular disease. Of the hypertensive COVID-19 patients, 51 (56.04%) were male. Multivariable analysis showed that old age, comorbid diabetes or coronary heart disease on admission, increased D-dimer, increased glucose, and decreased lymphocyte count were independent risk factors associated with hypertensive COVID-19 patients. Elevated total bilirubin (odds ratio [OR]: 1.014, 95% confidence interval [CI]: 0.23–1.05; p=0.043) and triglycerides (OR: 1.173, 95% CI: 0.049–1.617; p=0.007) were found to be associated with elderly hypertensive COVID-19 patients. In addition, we found that decreased lymphocytes, basophil, high-density lipoprotein, and increased fibrinogen and creatinine were related to a higher risk of disease severity in male patients. The most common abnormal clinical findings pertaining to female hypertensive COVID-19 patients were hemoglobin, total bile acid, total protein, and low-density lipoprotein. Conclusions. Factors associated with increased risk of hypertensive COVID-19 patients were identified. Results to the different age and sex subgroups in our study will allow for better possible personalized care and also provide new insights into specific risk stratification, disease management, and treatment strategies for COVID-19 patients with hypertension in the future.

BackgroundLeukemic stem cell (LSC) is thought to be responsible for chronic myelogenous leukemia (CML) initiation and relapse. However, the inherent regulation of LSCs remains largely obscure. Herein, we integratedly analyzed miRNA and gene expression alterations in bone marrow (BM) Lin-Sca1+c-Kit+ cells (LSKs) of a tet-off inducible CML mouse model, Scl/tTA-BCR/ABL (BA).MethodsScl/tTA and TRE-BA transgenic mice were crossed in the presence of doxycycline to get double transgenic mice. Both miRNA and mRNA expression profiles were generated from BM LSKs at 0 and 3 weeks after doxycycline withdrawal. The target genes of differentially expressed miRNAs were predicted, followed by the miRNA-mRNA network construction. In vitro and in vivo experiments were further performed to elucidate their regulation and function in CML progression.ResultsAs a result of the integrated analysis and experimental validation, an anti-apoptotic pathway emerged from the fog. miR-142a was identified to be downregulated by enhanced ERK-phosphorylation in BA-harboring cells, thereby relieving its repression on Ciapin1, an apoptosis inhibitor. Moreover, miR-142a overexpression could partially rescue the abnormal anti-apoptotic phenotype and attenuate CML progression.ConclusionTaken together, this study explored the miRNA-mRNA regulatory networks in murine CML LSKs and demonstrated that ERK-miR-142a-Ciapin1 axis played an essential role in CML pathogenesis.

Background and Purpose. Diabetes is common in COVID-19 patients and associated with unfavorable outcomes. We aimed to describe the characteristics and identify the risk factors for COVID-19 patients complicated with diabetes. Methods. In this multicenter retrospective study, patients with COVID-19 in China were included and classified into two groups according to whether they were complicated with diabetes or not. Demographic symptoms and laboratory data were extracted from medical records. Univariable and multivariable logistic regression methods were used to explore the risk factors. Results. 538 COVID-19 patients were finally included in this study, of whom 492 were nondiabetes and 46 were diabetes. The median age was 47 years (IQR 35.0-56.0). And the elderly patients with diabetes were more likely to have dry cough, and the alanine aminotransferase, lactate dehydrogenase, Ca, and mean hemoglobin recovery rate were higher than the other groups. Furthermore, we also found the liver and kidney function of male patients was worse than that of female patients, while female cases should be paid more attention to the occurrence of bleeding and electrolyte disorders. Moreover, advance age, blood glucose, gender, prothrombin time, and total cholesterol could be considered as risk factors for COVID-19 patients with diabetes through the multivariable logistic regression model in our study. Conclusion. The potential risk factors found in our study showed a major piece of the complex puzzle linking diabetes and COVID-19 infection. Meanwhile, focusing on gender and age factors in COVID-19 patients with or without diabetes, specific clinical characteristics, and risk factors should be paid more attention by clinicians to figure out a targeted intervention to improve clinical efficacy worldwide.

BackgroundA worldwide outbreak of coronavirus disease (COVID-19), since 2019, has brought a disaster to people all over the world. Many researchers carried out clinical epidemiological studies on patients with COVID-19 previously, but risk factors for patients with different levels of severity are still unclear.Methods562 patients with laboratory-confirmed COVID-19 from 12 hospitals in China were included in this retrospective study. Related clinical information, therapies, and imaging data were extracted from electronic medical records and compared between patients with severe and non-severe status. We explored the risk factors associated with different severity of COVID-19 patients by logistic regression methods.ResultsBased on the guideline we cited, 509 patients were classified as non-severe and 53 were severe. The age range of whom was 5-87 years, with a median age of 47 (IQR 35.0-57.0). And the elderly patients (older than 60 years old) in non-severe group were more likely to suffer from fever and asthma, accompanied by higher level of D-dimer, red blood cell distribution width and low-density lipoprotein. Furthermore, we found that the liver and kidney function of male patients was worse than that of female patients in both severe and non-severe groups with different age levels, while the severe females had faster ESR and lower inflammatory markers. Of major laboratory markers in non-severe cases, baseline albumin and the lymphocyte percentage were higher, while the white blood cell and the neutrophil count were lower. In addition, severe patients were more likely to be accompanied by an increase in cystatin C, mean hemoglobin level and a decrease in oxygen saturation. Besides that, advanced age and indicators such as count of white blood cell, glucose were proved to be the most common risk factors preventing COVID-19 patients from aggravating.ConclusionThe potential risk factors found in our study have shown great significance to prevent COVID-19 patients from aggravating and turning to critical cases during treatment. Meanwhile, focusing on gender and age factors in groups with different severity of COVID-19, and paying more attention to specific clinical symptoms and characteristics, could improve efficacy of personalized intervention to treat COVID-19 effectively.

Setd2 is known as a histone-H3K36-specific methyltransferase. However, its role in physiological function remains unclear. In this study, we show that Setd2 mainly regulates differentiation of murine embryonic stem cells (mESCs) toward primitive endoderm. Furthermore, we show that downregulated endoderm-related genes in Setd2−/− mESCs are associated with an aberrantly low level of Erk activity and that enforced expression of Fgfr3 can rescue the defective Erk pathway in Setd2−/− mESCs. Interestingly, the transcriptional initiation of Fgfr3 is directly regulated through histone H3K36me3 modification in its distal promoter region by Setd2. These results indicate that Setd2 controls the primitive endoderm differentiation of mESCs by regulating the Fgfr3-Erk signaling.