Interferon-β-induced miR-155 inhibits osteoclast differentiation by targeting SOCS1 and MITF

AbstractIFN-β is induced via a c-fos dependent mechanism that is present downstream of the receptor activator of NF-κB ligand (RANKL)-RANK signal transduction cascade during osteoclast differentiation. Increased production of IFN-β in turn inhibits osteoclastogenesis. However, the mechanism by which IFN-β exerts its suppressive function remains unclear. In the present study, we found that miR-155, an IFN-β-induced miRNA, mediated the suppressive effect of IFN-β on osteoclast differentiation by targeting SOCS1 and MITF, two essential regulators of osteoclastogenesis. These findings have not only demonstrated that miR-155 inhibits osteoclast differentiation, but also provided a new therapeutic target for treatment of osteoclast-mediated diseases

Dementia-related adverse events in PARADIGM-HF and other trials in heart failure with reduced ejection fraction.

Aims: Inhibition of neprilysin, an enzyme degrading natriuretic and other vasoactive peptides, is beneficial in heart failure with reduced ejection fraction (HFrEF), as shown in PARADIGM-HF which compared the angiotensin receptor–neprilysin inhibitor (ARNI) sacubitril/valsartan with enalapril. As neprilysin is also one of many enzymes clearing amyloid-β peptides from the brain, there is a theoretical concern about the long-term effects of sacubitril/valsartan on cognition. Therefore, we have examined dementia-related adverse effects (AEs) in PARADIGM-HF and placed these findings in the context of other recently conducted HFrEF trials. Methods and results: In PARADIGM-HF, patients with symptomatic HFrEF were randomized to sacubitril/valsartan 97/103 mg b.i.d. or enalapril 10 mg b.i.d. in a 1:1 ratio. We systematically searched AE reports, coded using the Medical Dictionary for Regulatory Activities (MedDRA), using Standardized MedDRA Queries (SMQs) with ‘broad’ and ‘narrow’ preferred terms related to dementia. In PARADIGM-HF, 8399 patients aged 18–96 years were randomized and followed for a median of 2.25 years (up to 4.3 years). The narrow SMQ search identified 27 dementia-related AEs: 15 (0.36%) on enalapril and 12 (0.29%) on sacubitril/valsartan [hazard ratio (HR) 0.73, 95% confidence interval (CI) 0.33–1.59]. The broad search identified 97 (2.30%) and 104 (2.48%) AEs (HR 1.01, 95% CI 0.75–1.37), respectively. The rates of dementia-related AEs in both treatment groups in PARADIGM-HF were similar to those in three other recent trials in HFrEF. Conclusion: We found no evidence that sacubitril/valsartan, compared with enalapril, increased dementia-related AEs, although longer follow-up may be necessary to detect such a signal and more sensitive tools are needed to detect lesser degrees of cognitive impairment. Further studies to address this question are warranted

Development and external validation of prognostic models to predict sudden and pump-failure death in patients with HFrEF from PARADIGM-HF and ATMOSPHERE

Background: Sudden death (SD) and pump failure death (PFD) are the two leading causes of death in patients with heart failure and reduced ejection fraction (HFrEF). Objective: Identifying patients at higher risk for mode-specific death would allow better targeting of individual patients for relevant device and other therapies. Methods: We developed models in 7156 patients with HFrEF from the Prospective comparison of ARNI with ACEI to Determine Impact on Global Mortality and morbidity in Heart Failure (PARADIGM-HF) trial, using Fine-Gray regressions counting other deaths as competing risks. The derived models were externally validated in the Aliskiren Trial to Minimize Outcomes in Patients with Heart Failure (ATMOSPHERE) trial. Results: NYHA class and NT-proBNP were independent predictors for both modes of death. The SD model additionally included male sex, Asian or Black race, prior CABG or PCI, cancer history, MI history, treatment with LCZ696 vs. enalapril, QRS duration and ECG left ventricular hypertrophy. While LVEF, ischemic etiology, systolic blood pressure, HF duration, ECG bundle branch block, and serum albumin, chloride and creatinine were included in the PFD model. Model discrimination was good for SD and excellent for PFD with Harrell’s C of 0.67 and 0.78 after correction for optimism, respectively. The observed and predicted incidences were similar in each quartile of risk scores at 3 years in each model. The performance of both models remained robust in ATMOSPHERE. Conclusion: We developed and validated models which separately predict SD and PFD in patients with HFrEF. These models may help clinicians and patients consider therapies targeted at these modes of death. Trial registration number: PARADIGM-HF: ClinicalTrials.gov NCT01035255, ATMOSPHERE: ClinicalTrials.gov NCT00853658

Algorithms of approximate dynamic programming for hydro scheduling

In hydro scheduling, unit commitment is a complex sub-problem. This paper proposes a new approximate dynamic programming technique to solve unit commitment. A new method called Least Square Policy Iteration (LSPI) algorithm is introduced which is efficient and faster in convergence. This algorithm takes the properties of widely used algorithm least square temporal difference (LSTD), enhance it further and make it useful for optimization problems. First value function is to find a fixed policy by using least square temporal difference Q (LSTDQ) algorithm which is similar to LSTD, then LSPI is introduced for making the policy iteration algorithm by using the results of LSTDQ. It combines the data efficiency of LSTDQ and policy-search efficiency of policy iteration

Speedup improvement on general connectivity computation by algorithmic techniques and parallel processing

Proceedings of the Conference on High Performance Computing on the Information Superhighway, HPC Asia'97724-72725

Investigation on the Physical and Mental Health of College Students Based on Statistics

Contemporary college students face pressures from employment, emotion, and academic work, so the physical and mental health of college students has gradually become the focus of social attention. Through a questionnaire survey, this paper investigates five aspects : college students’sense of physical health, physical exercise, psychological distress, stress resistance and psychological adjustment methods. College students’ physical and mental health has been analyzed from the basic situation, differences in different student groups, main problems and suggestions, trying to objectively reflect the physical and mental health of college students and provide a basis for the development of college students’ideological and political education

A modular parallelization framework for power flow transfer analysis of large-scale power systems

Abstract Power flow transfer (PFT) analysis under various anticipated faults in advance is important for securing power system operations. In China, PSD-BPA software is the most widely used tool for power system analysis, but its input/output interface is easily adapted for PFT analysis, which is also difficult due to its computationally intensity. To solve this issue, and achieve a fast and accurate PFT analysis, a modular parallelization framework is developed in this paper. Two major contributions are included. One is several integrated PFT analysis modules, including parameter initialization, fault setting, network integrity detection, reasonableness identification and result analysis. The other is a parallelization technique for enhancing computation efficiency using a Fork/Join framework. The proposed framework has been tested and validated by the IEEE 39 bus reference power system. Furthermore, it has been applied to a practical power network with 11052 buses and 12487 branches in the Yunnan Power Grid of China, providing decision support for large-scale power system analysis

Modeling and Solution Techniques Used for Hydro Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested

Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant

The thirst of the Earth for energy is lurching towards catastrophe in an era of increasing water shortage where most of the power plants are hydroelectric. The hydro-based power systems are facing challenges in determining day-ahead generation schedules of cascaded hydropower plants. The objective of the current study is to find a speedy and practical method for predicting and classifying the future schedules of hydropower plants in order to increase the overall efficiency of energy by utilizing the water of cascaded hydropower plants. This study is significant for water resource planners in the planning and management of reservoirs for generating energy. The proposed method consists of data mining techniques and approaches. The energy production relationship is first determined for upstream and downstream hydropower plants by using multiple linear regression. Then, a cluster analysis is used to find typical generation curves with the help of historical data. The decision tree algorithm C4.5, Iterative Dichotomiser 3-IV, improved C4.5 and Chi-Squared Automatic Interaction Detection are adopted to quickly predict generation schedules, and detailed comparison among different algorithms are made. The decision tree algorithms are solved using SIPINA software. Results show that the C4.5 algorithm is more feasible for rapidly generating the schedules of cascaded hydropower plants. This decision tree algorithm is helpful for the researchers to make fast decisions in order to enhance the energy production of cascaded hydropower plants. The major elements of this paper are challenges and solution of head sensitive hydropower plants, using the decision-making algorithms for producing the generation schedules, and comparing the generation from the proposed method with actual energy production

Peak Operation of Cascaded Hydropower Plants Serving Multiple Provinces

The bulk hydropower transmission via trans-provincial and trans-regional power networks in China provides great operational flexibility to dispatch power resources between multiple power grids. This is very beneficial to alleviate the tremendous peak load pressure of most provincial power grids. This study places the focus on peak operations of cascaded hydropower plants serving multiple provinces under a regional connected AC/DC network. The objective is to respond to peak loads of multiple provincial power grids simultaneously. A two-stage search method is developed for this problem. In the first stage, a load reconstruction strategy is proposed to combine multiple load curves of power grids into a total load curve. The purpose is to deal with different load features in load magnitudes, peaks and valleys. A mutative-scale optimization method is then used to determine the generation schedules of hydropower plants. In the second stage, an exterior point search method is established to allocate the generation among multiple receiving power grids. This method produces an initial solution using the load shedding algorithm, and further improves it by iteratively coordinating the generation among different power grids. The proposed method was implemented to the operations of cascaded hydropower plants on Xin-Fu River and another on Hongshui River. The optimization results in two cases satisfied the peak demands of receiving provincial power grids. Moreover, the maximum load difference between peak and valley decreased 12.67% and 11.32% in Shanghai Power Grid (SHPG) and Zhejiang Power Grid (ZJPG), exceeding by 4.85% and 6.72% those of the current operational method, respectively. The advantage of the proposed method in alleviating peak-shaving pressure is demonstrated