Impact of resistance exercise on ribosome biogenesis is acutely regulated by post-exercise recovery strategies

Muscle hypertrophy occurs following increased protein synthesis, which requires activation of the ribosomal complex. Additionally, increased translational capacity via elevated ribosomal RNA (rRNA) synthesis has also been implicated in resistance training-induced skeletal muscle hypertrophy. The time course of ribosome biogenesis following resistance exercise (RE) and the impact exerted by differing recovery strategies remains unknown. In the present study, the activation of transcriptional regulators, the expression levels of pre-rRNA, and mature rRNA components were measured through 48 h after a single-bout RE. In addition, the effects of either low-intensity cycling (active recovery, ACT) or a cold-water immersion (CWI) recovery strategy were compared. Nine male subjects performed two bouts of high-load RE randomized to be followed by 10 min of either ACT or CWI. Muscle biopsies were collected before RE and at 2, 24, and 48 h after RE. RE increased the phosphorylation of the p38-MNK1-eIF4E axis, an effect only evident with ACT recovery. Downstream, cyclin D1 protein, total eIF4E, upstream binding factor 1 (UBF1), and c-Myc proteins were all increased only after RE with ACT. This corresponded with elevated abundance of the pre-rRNAs (45S, ITS-28S, ITS-5.8S, and ETS-18S) from 24 h after RE with ACT. In conclusion, coordinated upstream signaling and activation of transcriptional factors stimulated pre-rRNA expression after RE. CWI, as a recovery strategy, markedly blunted these events, suggesting that suppressed ribosome biogenesis may be one factor contributing to the impaired hypertrophic response observed when CWI is used regularly after exercise.

Geometry-free stochastic analysis of BDS triple frequency signals

The paper presents a geometry-free approach to assess the variation of covariance matrices of undifferenced triple frequency GNSS measurements and its impact on positioning solutions. Four independent geometryfree/ ionosphere-free (GFIF) models formed from original triple-frequency code and phase signals allow for effective computation of variance-covariance matrices using real data. Variance Component Estimation (VCE) algorithms are implemented to obtain the covariance matrices for three pseudorange and three carrier-phase signals epoch-by-epoch. Covariance results from the triple frequency Beidou System (BDS) and GPS data sets demonstrate that the estimated standard deviation varies in consistence with the amplitude of actual GFIF error time series. The single point positioning (SPP) results from BDS ionosphere-free measurements at four MGEX stations demonstrate an improvement of up to about 50% in Up direction relative to the results based on a mean square statistics. Additionally, a more extensive SPP analysis at 95 global MGEX stations based on GPS ionosphere-free measurements shows an average improvement of about 10% relative to the traditional results. This finding provides a preliminary confirmation that adequate consideration of the variation of covariance leads to the improvement of GNSS state solutions.

Optimisation techniques for planning automatic under frequency load shedding in New Zealand’s power system

Electricity generation is vital in developed countries to power the many mechanical and electrical devices that people require. Unfortunately electricity generation is costly. Though electricity can be generated it cannot be stored efficiently. Electricity generation is also difficult to manage because exact demand is unknown from one instant to the next. A number of services are required to manage fluctuations in electricity demand, and to protect the system when frequency falls too low. A current approach is called automatic under frequency load shedding (AUFLS). This article proposes new methods for optimising AUFLS in New Zealand’s power system. The core ideas were developed during the 2015 Maths and Industry Study Group (MISG) in Brisbane, Australia. The problem has been motivated by Transpower Limited, a company that manages New Zealand’s power system and transports bulk electricity from where it is generated to where it is needed. The approaches developed in this article can be used in electrical power systems anywhere in the world.

The Pandolf load carriage equation is a poor predictor of metabolic rate while wearing explosive ordnance disposal protective clothing

This investigation aimed to quantify metabolic rate when wearing an explosive ordnance disposal (EOD) ensemble (~33kg) during standing and locomotion; and determine whether the Pandolf load carriage equation accurately predicts metabolic rate when wearing an EOD ensemble during standing and locomotion. Ten males completed 8 trials with metabolic rate measured through indirect calorimetry. Walking in EOD at 2.5, 4.0 and 5.5km·h−1 was significantly (p < 0.05) greater than matched trials without the EOD ensemble by 49% (127W), 65% (213W) and 78% (345W), respectively. Mean bias (95% limits of agreement) between predicted and measured metabolism during standing, 2.5, 4 and 5.5km·h−1 were 47W (19 to 75W); −111W (−172 to −49W); −122W (−189 to −54W) and −158W (−245 to −72W), respectively. The Pandolf equation significantly underestimated measured metabolic rate during locomotion. These findings have practical implications for EOD technicians during training and operation and should be considered when developing maximum workload duration models and work-rest schedules.

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.

First-Line afatinib versus chemotherapy in patients with non–small cell lung cancer and common epidermal growth factor receptor gene mutations and brain metastases

Introduction Metastatic spread to the brain is common in patients with non–small cell lung cancer (NSCLC), but these patients are generally excluded from prospective clinical trials. The studies, phase III study of afatinib or cisplatin plus pemetrexed in patients with metastatic lung adenocarcinoma with EGFR mutations (LUX-Lung 3) and a randomized, open-label, phase III study of BIBW 2992 versus chemotherapy as first-line treatment for patients with stage IIIB or IV adenocarcinoma of the lung harbouring an EGFR activating mutation (LUX-Lung 6) investigated first-line afatinib versus platinum-based chemotherapy in epidermal growth factor receptor gene (EGFR) mutation-positive patients with NSCLC and included patients with brain metastases; prespecified subgroup analyses are assessed in this article. Methods For both LUX-Lung 3 and LUX-Lung 6, prespecified subgroup analyses of progression-free survival (PFS), overall survival, and objective response rate were undertaken in patients with asymptomatic brain metastases at baseline (n = 35 and n = 46, respectively). Post hoc analyses of clinical outcomes was undertaken in the combined data set (n = 81). Results In both studies, there was a trend toward improved PFS with afatinib versus chemotherapy in patients with brain metastases (LUX-Lung 3: 11.1 versus 5.4 months, hazard ratio [HR] = 0.54, p = 0.1378; LUX-Lung 6: 8.2 versus 4.7 months, HR = 0.47, p = 0.1060). The magnitude of PFS improvement with afatinib was similar to that observed in patients without brain metastases. In combined analysis, PFS was significantly improved with afatinib versus with chemotherapy in patients with brain metastases (8.2 versus 5.4 months; HR, 0.50; p = 0.0297). Afatinib significantly improved the objective response rate versus chemotherapy in patients with brain metastases. Safety findings were consistent with previous reports. Conclusions These findings lend support to the clinical activity of afatinib in EGFR mutation–positive patients with NSCLC and asymptomatic brain metastases.

Targeting the fibroblast growth factor receptor family in cancer

Fibroblast growth factors (FGFs) regulate a plethora of biological functions, in both the embryonic and adult stages of development, binding their cognate receptors and thus activating a variety of downstream signalling pathways. Deregulation of the FGF/FGFR signalling axis, observed in multifarious tumor types including squamous non-small cell lung cancer, occurs through genomic FGFR alterations that drive ligand-independent receptor signalling or alterations that support ligand-dependent activation. Mutations are not restricted to the tyrosine kinase domain and aberrations appear to be tumor type dependent. As well as its complementarity and synergy with VEGF of particular interest is the interplay between FGFR and EGFR and the ability of these pathways to offer a compensatory signalling escape mechanism when either is inhibited. Hence there exists a rationale for a combinatorial approach to inhibition of these dysregulated pathways to reverse drug resistance. To date, several multi-target tyrosine kinase inhibitors as well as FGFR specific tyrosine kinase inhibitors (TKIs), monoclonal antibodies and FGF ligand traps have been developed. Promising preclinical data has resulted in several drugs entering clinical trials. This review explores aberrant FGFR and its potential as a therapeutic target in solid tumors.

Mutational analysis of the insulin‑like growth factor 1 receptor tyrosine kinase domain in non-small cell lung cancer patients

The insulin‑like growth factor 1 receptor (IGF1R) pathway plays an important role in the pathogenesis of non‑small cell lung cancer (NSCLC) and also provides a mechanism of resistance to targeted therapies. IGF1R is therefore an ideal therapeutic target and several inhibitors have entered clinical trials. However, thus far the response to these inhibitors has been poor, highlighting the importance of predictive biomarkers to identify patient cohorts who will benefit from these targeted agents. It is well‑documented that mutations and/or deletions in the epidermal growth factor receptor (EGFR) tyrosine kinase (TK) domain predict sensitivity of NSCLC patients to EGFR TK inhibitors. Single‑nucleotide polymorphisms (SNPs) in the IGF pathway have been associated with disease, including breast and prostate cancer. The aim of the present study was to elucidate whether the IGF1R TK domain harbours SNPs, somatic mutations or deletions in NSCLC patients and correlates the mutation status to patient clinicopathological data and prognosis. Initially 100 NSCLC patients were screened for mutations/deletions in the IGF1R TK domain (exons 16‑21) by sequencing analysis. Following the identification of SNP rs2229765, a further 98 NSCLC patients and 866 healthy disease‑free control patients were genotyped using an SNP assay. The synonymous SNP (rs2229765) was the only aberrant base change identified in the IGF1R TK domain of 100 NSCLC patients initially analysed. SNP rs2229765 was detected in exon 16 and was found to have no significant association between IGF1R expression and survival. The GA genotype was identified in 53.5 and 49.4% of NSCLC patients and control individuals, respectively. No significant difference was found in the genotype (P=0.5487) or allele (P=0.9082) frequencies between the case and control group. The present findings indicate that in contrast to the EGFR TK domain, the IGF1R TK domain is not frequently mutated in NSCLC patients. The synonymous SNP (rs2229765) had no significant association between IGF1R expression and survival in the cohort of NSCLC patients.