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This study was designed to find the relationship between adolescent Golf players’ achievement goal orientation, exercise flow and player’s satisfaction. To test this 237 middle and high school Golf players who are registered in Korea Golf Association are sampled. The resulting data were processed by means of frequency analysis, confirmatory factor analysis, correlation analysis, structure modeling analysis using SPSS 21.0 and AMOS 21.0 version program. Results derived from the researches based on the aforementioned methods and data analyses are as follows: First, adolescent Golf players’ task goal orientation and self goal orientation which are sub factors of achievement goal orientation had positive effect on exercise flow. Second, adolescent Golf players’ the task goal orientation sub factor of achievement goal orientation had positive effect on player’s satisfaction. Third, adolescent Golf players’ exercise flow had positive effect on player’s satisfaction.

Alcohol consumption is a critical risk factor for hepatic pathogenesis, including alcoholic liver diseases (ALD), but implications of alcohol-induced dysregulation of microRNA (miRNA) in ALD pathogenesis are not completely understood. In the present study, C57BL/6J male mice were treated with saline (CON; oral gavage; n = 8) or alcohol (EtOH; 3 g/kg body weight; oral gavage; n = 8) for 7 days. A total of 599 miRNAs and 158 key mRNAs related to fatty liver and hepatotoxicity pathways were assessed in mice liver tissues. The mRNA expression datasets were then utilized to predict interactions with miRNAs that were changed by alcohol consumption. Predicted miRNA-mRNA interactions were validated using in vitro miRNA transfection experiments. The results showed that let-7a was significantly decreased in the EtOH group and Rb1 mRNA was predicted as a target gene. This was further supported by an inverse correlation of RB1 and let-7a expression in mice liver tissue. Additionally, key protein expressions involved in RB1-apoptosis axis [i.e., p73, cleaved CASP-3 (cCASP-3), and cCASP-7] showed a trend of increase in the EtOH mice; this was also confirmed by capase-3 enzyme activity and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling assay in livers of mice that had consumed alcohol. In line with our in vivo observations, alcohol treatment suppressed the let-7a expression and subsequently upregulated p73, cCASP-3, and cCASP-7 protein expressions in mice hepatocytes. Additional proteins in the apoptosis regulatory pathway (i.e., MDM2-p53 axis) were significantly changed in response to let-7a suppression in the cells. Taken together, the current study provides mechanistic evidence that alcohol consumption-induced let-7a suppression results in the upregulation of RB1, thereby promoting hepatic apoptosis through induction of pro-apoptotic proteins (e.g., p73), and by, at least in part, preventing MDM2-mediated p53 degradation.

The adapter protein SH2B1 is recruited to neurotrophin receptors, including TrkB (also known as NTRK2), the receptor for brain-derived neurotrophic factor (BDNF). Herein, we demonstrate that the four alternatively spliced isoforms of SH2B1 (SH2B1α–SH2B1δ) are important determinants of neuronal architecture and neurotrophin-induced gene expression. Primary hippocampal neurons from Sh2b1−/− [knockout (KO)] mice exhibit decreased neurite complexity and length, and BDNF-induced expression of the synapse-related immediate early genes Egr1 and Arc. Reintroduction of each SH2B1 isoform into KO neurons increases neurite complexity; the brain-specific δ isoform also increases total neurite length. Human obesity-associated variants, when expressed in SH2B1δ, alter neurite complexity, suggesting that a decrease or increase in neurite branching may have deleterious effects that contribute to the severe childhood obesity and neurobehavioral abnormalities associated with these variants. Surprisingly, in contrast to SH2B1α, SH2B1β and SH2B1γ, which localize primarily in the cytoplasm and plasma membrane, SH2B1δ resides primarily in nucleoli. Some SH2B1δ is also present in the plasma membrane and nucleus. Nucleolar localization, driven by two highly basic regions unique to SH2B1δ, is required for SH2B1δ to maximally increase neurite complexity and BDNF-induced expression of Egr1, Arc and FosL1.

Pediatric high-grade glioma (pHGG) is an incurable disease with a median survival of less than 6 months post-progression and no effective targeted therapy. PDGFRA is commonly altered in pHGG, but targeting PDGFRA in this disease has been unsuccessful, likely due to poor central nervous system (CNS) penetrance. Avapritinib is a novel and CNS-penetrant PDGFRA/KIT inhibitor that is FDA-approved for adults with unresectable or metastatic PDGFRA exon 18-mutant gastrointestinal stromal tumor (GIST) and is being studied in CNS tumors. We performed a pre-clinical and clinical assessment to determine the potential suitability of avapritinib therapy in PDGFRA-driven glioma. A multi-institutional cohort genetic analysis revealed PDGFRA amplification and mutation in 10.2% and 6.1% of pHGG, respectively. Additionally, PDGFRA expression in the absence of genetic events was significantly increased in H3K27-altered diffuse midline glioma (DMG) compared to H3-wildtype pHGG. Avapritinib performed well in: (i) mutant PDGFRA enzyme inhibition and wildtype inhibition at high dose, (ii) minimal off-target kinase inhibition, (iii) brain penetration (peak 10 µM), and (iv) proliferation/pPDGFRA reduction in PDGFRA-amplified and mutant pHGG cell lines. Avapritinib treatment in an aggressive PDX model of pHGG resulted in significant survival benefit. We pursued treatment of eight pediatric and young adult HGG patients with avapritinib across seven institutions. Patients were a mixture of local (N = 4) and metastatic disease (N = 4); all patients were post-initial radiation, with 7/8 having progressed on prior treatment. 7/8 patients had PDGFRA amplifications or mutations, and 7/8 had H3K27M mutations. Therapy was generally well-tolerated. 4/8 patients showed radiographic response to avapritinib, with one patient demonstrating complete response of target lesion and remains on therapy. Avapritinib levels in patients’ CSF and brain tumor tissue reached micromolar levels. These results demonstrate that avapritinib is a potent, selective, and CNS-penetrant PDGFRA/KIT inhibitor that is promising for further study in pHGG with relevant alterations.

This paper presents a stochastic method for an electric vehicle (EV) aggregator to coordinate EV charging schedule considering uncertainty in EV departures. The EV aggregator is responsible for managing the charging schedule of EVs while participating in the electricity markets. The managed EV charging can provide additional revenues to the aggregator from regulation market participation and charging cost reductions to EV owners. The aggregator needs to coordinate the charging schedule considering various uncertain factors such as electricity market prices and the stochastic characteristics of EVs. In this paper, the EV charging scheduling problem incorporating uncertainty in EV departures is formulated as a stochastic optimization problem. A stochastic optimization method is used to solve the EV charging scheduling problem. Latin hypercube sampling (LHS) and a scenario-reduction method are also applied to reduce the computational efforts of the proposed method. The results of a numerical example are presented to show the effectiveness of the proposed stochastic EV charging coordination method.

Demand-side management (DSM) is easy to apply to reduce system peak load by a utility and it can be a convenient way to control and change amount of electric usage by end-use customers. Planning and operating techniques for a DSM program are required to efficiently manage and operate the program. This paper is focused on planning technique for an incentive-based DSM program. This paper describes a stochastic model that can estimate the operating days, hours, and total capacity for efficiently planning a DSM program. A temperature stochastic process, from weather derivatives, is used in the proposed method. Temperature sensitivity is proposed to improve load forecasting accuracy. The generalized extreme value distribution is also proposed for estimating stochastic results. The results of case studies are presented to show the effectiveness of the proposed method.

Superconducting cables can be used as an alternative to replace the conventional ac cables in a power system. Because of the characteristics of superconducting cables, their installation in a power system can influence the fault current levels and patterns. These changes in fault current levels and patterns can further have an impact on power system protection. Therefore, in order to prevent resulting problems, there is an increasing need to analyze the impact of a superconducting cable on the power system and determine its impedance under fault conditions. This paper presents a systematic impedance selection method for a superconducting cable under fault conditions, based on the evaluation results of the impact of superconducting cables on power system protection. Numerical simulations are carried out to demonstrate the effectiveness of the proposed method.

Superconducting fault current limiters (SFCLs) are increasingly being used as a mechanism to effectively control fault currents in a power system. However, SFCL placement and relay coordination can be complicated by the unexpected failure or maintenance of SFCLs. This paper presents a power system reconfiguration method for coordinating overcurrent protection considering SFCL failure in system protection. The proposed power system reconfiguration method is designed to handle SFCL failure in the relay coordination.

Various load disaggregation methods have been developed for monitoring home appliance loads to save energy in a smart home. Most of load disaggregation methods are designed to focus on the on/off events of single appliance. In reality, however, multiple appliances can be turned on/off simultaneously. Load disaggregation can be complicated by the simultaneous on/off events of multiple appliances. This paper presents a cepstrum-smoothing-based load disaggregation method to effectively deal with the simultaneous on/off events of multiple appliances. Further, a data acquisition system is developed to obtain only the characteristic signals of appliances and to filter the noise inputted from the power supply. Test results are provided to demonstrate the effectiveness of the proposed cepstrumsmoothing- based load disaggregation method1.

Background: Although small non-coding RNAs are mostly encoded by the nuclear genome, thousands of small non-coding RNAs encoded by the mitochondrial genome, termed as mitosRNAs were recently reported in human, mouse and trout. In this study, we first identified chicken mitosRNAs in breast muscle using small RNA sequencing method and the differential abundance was analyzed between modern pedigree male (PeM) broilers (characterized by rapid growth and large muscle mass) and the foundational Barred Plymouth Rock (BPR) chickens (characterized by slow growth and small muscle mass).Methods: Small RNA sequencing was performed with total RNAs extracted from breast muscles of PeM and BPR (n = 6 per group) using the 1 × 50 bp single end read method of Illumina sequencing. Raw reads were processed by quality assessment, adapter trimming, and alignment to the chicken mitochondrial genome (GenBank Accession: X52392.1) using the NGen program. Further statistical analyses were performed using the JMP Genomics 8. Differentially expressed (DE) mitosRNAs between PeM and BPR were confirmed by quantitative PCR.Results: Totals of 183,416 unique small RNA sequences were identified as potential chicken mitosRNAs. After stringent filtering processes, 117 mitosRNAs showing >100 raw read counts were abundantly produced from all 37 mitochondrial genes (except D-loop region) and the length of mitosRNAs ranged from 22 to 46 nucleotides. Of those, abundance of 44 mitosRNAs were significantly altered in breast muscles of PeM compared to those of BPR: all mitosRNAs were higher in PeM breast except those produced from 16S-rRNA gene. Possibly, the higher mitosRNAs abundance in PeM breast may be due to a higher mitochondrial content compared to BPR. Our data demonstrate that in addition to 37 known mitochondrial genes, the mitochondrial genome also encodes abundant mitosRNAs, that may play an important regulatory role in muscle growth via mitochondrial gene expression control.

This paper presents Cepstrum smoothing-based feature extraction method to reduce the variation of the characteristic signals of home appliance for Nonintrusive Load Monitoring (NILM). In this paper, a data acquisition system is also developed to obtain only characteristic signals of appliances and to filter the noise inputted from the power supply. Testing results are provided to demonstrate the effectiveness of the proposed method.

The effective use of energy is emphasized in a smart home environment because of concerns regarding growing energy demands. To save energy in a smart home, detailed energy consumption information at the appliance level needs to be monitored and delivered to consumers. Unlike distributed direct sensing, non-intrusive load monitoring (NILM) is receiving increased attention as a low-cost, low-complexity load motoring technique. This paper presents an electrical event identification technique for monitoring home appliance load using appliance load signatures. The load signature acquisition system is also introduced in the paper.

With an increasing concern about environmental pollution and rising price of fossil fuels, electric vehicles (EVs) are becoming an important alternative energy source in a transportation sector. A rapid deployment of EVs may have significant impacts on demand for electricity in a power system. Also, a large-scale deployment of EVs can introduce greater uncertainty in EV charging patterns and loads. This paper presents a Stratified Latin Hypercube Sampling (SLHS)-based probabilistic load flow (PLF) method incorporating electric vehicle charging load. In this paper, probabilistic EV charging load is modeled by using the EV penetration level, hourly traffic patterns, and EV charging scenarios. The Monte Carlo Simulation (MCS)-based PLF requires a significant amount of computation time to obtain accurate results. In this paper, SLHS technique is also applied to reduce the computation time of the PLF. A numerical example is presented to show the performance of the proposed method.