Oestrogen-modulated gene expression in the human endometrium

To identify key regulatory mechanisms in the growth and development of the human endometrium, microarray analysis was performed on uncultured human endometrium collected during menstruation (M) and the late-proliferative (LATE-P)-phase of the menstrual cycle, as well as after 24 h incubation in the presence of oestradiol (17beta-E2). We demonstrate the expression of novel gene transcripts in the human endometrium. i.e. mucin-9, novel oestrogen-responsive gene transcripts, i.e. gelsolin and flotillin-1, and genes known to be expressed in human endometrium but not yet shown to be oestrogen responsive, i.e. connexin-37 and TFF1/pS2. Genes reported to be expressed during the implantation window and implicated in progesterone action, i.e. secretoglobin family 2A, member 2 (mammaglobin) and homeobox-containing proteins, were up-regulated in uncultured LATE-P-phase endometrium compared to M-phase endometrium. Some gene transcripts are regulated directly by 17beta-E2 alone, others are influenced by the in vivo environment as well. These observations emphasise that the regulation of endometrium maturation by oestrogen entails more then just stimulation of cell proliferation.

The effects of exercise and adipose tissue lipolysis on plasma adiponectin concentration and adiponectin receptor expression in human skeletal muscle

Objective It has been suggested that adiponectin regulates plasma free fatty acid (FFA) clearance by stimulating FFA uptake and/or oxidation in muscle. We aimed to determine changes in plasma adiponectin concentration and adiponectin receptor 1 and 2 mRNA expression in skeletal muscle during and after prolonged exercise under normal, fasting conditions (high FFA trial; HFA) and following pharmacological inhibition of adipose tissue lipolysis (low FFA trial; LFA). Furthermore, we aimed to detect and locate adiponectin in skeletal muscle tissue. Methods Ten subjects performed two exercise trials (120 min at 50% VO2max). Indirect calorimetry was used to determine total fat oxidation rate. Plasma samples were collected at rest, during exercise and during post-exercise recovery to determine adiponectin, FFA and glycerol concentrations. Muscle biopsies were taken to determine adiponectin protein and adiponectin receptor 1 and 2 mRNA expression and to localise intramyocellular adiponectin. Results Basal plasma adiponectin concentrations averaged 6.57±0.7 and 6.63±0.8 mg/l in the HFA and LFA trials respectively, and did not change significantly during or after exercise. In the LFA trial, plasma FFA concentrations and total fat oxidation rates were substantially reduced. However, plasma adiponectin and muscle adiponectin receptor 1 and 2 mRNA expression did not differ between trials. Immunohistochemical staining of muscle cross-sections showed the presence of adiponectin in the sarcolemma of individual muscle fibres and within the interfibrillar arterioles. Conclusion Plasma adiponectin concentrations and adiponectin receptor 1 and 2 mRNA expression in muscle are not acutely regulated by changes in adipose tissue lipolysis and/or plasma FFA concentrations. Adiponectin is abundantly expressed in muscle, and, for the first time, it has been shown to be present in/on the sarcolemma of individual muscle fibres.

Expression and regulation of vascular endothelial growth factor ligands and receptors during menstruation and post-menstrual repair of human endometrium

Regeneration and growth of the human endometrium after shedding of the functional layer during menstruation depends on an adequate angiogenic response. We analysed the mRNA expression levels of all known vascular endothelial growth factor (VEGF) ligands and receptors in human endometrium collected in the menstrual and proliferative phases of the menstrual cycle. In addition, we evaluated the expression of VEGF-A, VEGF-R2 and NRP-1 at the protein level. Two periods of elevated mRNA expression of ligands and receptors were observed, separated by a distinct drop at cycle days (CDs) 9 and 10. Immunohistochemical staining showed that VEGF and VEGF-R2 were expressed in epithelial, stromal and endothelial cells. NRP-1 was mainly confined to stroma and blood vessels; only in late-proliferative endometrium, epithelial staining was also observed. Except for endothelial VEGF-R2 expression in CDs 6-8, there were no significant differences in the expression of VEGF, VEGF-R2 or NRP-1 in any of the cell compartments. In contrast, VEGF release by cultured human endometrium explants decreased during the proliferative phase. This output was significantly reduced in menstrual and early-proliferative endometrium by estradiol (E2) treatment. Western blot analysis indicated that part of the VEGF-A was trapped in the extracellular matrix (ECM). Changes in VEGF ligands and receptors were associated with elevated expression of the hypoxia markers HIF1 alpha and CA-IX in the menstrual and early proliferative phases. HIF1 alpha was also detected in late-proliferative phase endometrium. Our findings indicate that VEGF-A exerts its actions mostly during the first half of the proliferative phase. Furthermore, VEGF-A production appears to be triggered by hypoxia in the menstrual phase and subsequently suppressed toy estrogen during the late proliferative phase.

Estrogen and the endometrium: lessons learned from gene expression profiling in rodents and human

To date, research into the biological processes and molecular mechanisms associated with endometrial receptivity and embryo implantation has been a focus of attention, whereas the complex events that occur in the human endometrium during the menstrual and proliferative phase under the influence of estrogen have received little attention. The objective of this review is to provide an update of our current understanding of the actions of estrogen on both human and rodent endometrium, with special emphasis on the regulation of uterine growth and cell proliferation, and the value of global gene expression analysis, in increasing understanding of these processes.

Haemoglobin expression in human endometrium

BACKGROUND: The general concept that haemoglobin is only a carrier protein for oxygen and carbon dioxide is challenged since recent studies have shown haemoglobin expression in non-erythroid cells and the protection of haemoglobin against oxidative and nitrosative stress. Using microarrays, we previously showed expression of haemoglobins alpha, beta, delta and gamma and the haeme metabolizing enzyme, haeme oxygenase (HO)-1 in human endometrium. METHODS: Using real-time quantitative PCR, haemoglobin alpha, beta, delta and gamma, and HO-1 mRNA levels were assessed throughout the menstrual cycle (n = 30 women). Haemoglobin and HO-1 protein levels in the human endometrium were assessed with immunohistochemistry. For steroid responsiveness, menstrual and late proliferative-phase endometrial explants were cultured for 24 h in the presence of vehicle (0.1% ethanol), estradiol (17 beta-E-2, 1 nM), progestin (Org 2058, 1 nM) or 17 beta-E-2+Org 2058 (1 nM each). RESULTS: All haemoglobins and the HO-1 were expressed in normal human endometrium. Haemoglobin mRNA and protein expression did not vary significantly during the menstrual cycle. Explant culture with Org 2058 or 17 beta-E-2+Org 2058 increased haemoglobin gamma mRNA expression (P < 0.05). HO-1 mRNA levels, and not protein levels, were significantly higher during the menstrual (M)-phase of the cycle (P < 0.05), and were down-regulated by Org 2058 in M-phase explants and by 17 beta-E-2+Org 2058 in LP-phase explants, versus control (P < 0.05). CONCLUSIONS: The haemoglobin-HO-1 system may be required to ensure adequate regulation of the bioavailability of haeme, iron and oxygen in human endometrium.

Relationship between EGFR expression, EGFR mutation status, and the efficacy of chemotherapy plus cetuximab in FLEX study patients with advanced non-small-cell lung cancer

INTRODUCTION: The phase III FLEX study (NCT00148798) in advanced non-small-cell lung cancer indicated that the survival benefit associated with the addition of cetuximab to cisplatin and vinorelbine was limited to patients whose tumors expressed high levels of epidermal growth factor receptor (EGFR) (immunohistochemistry score of >/=200; scale 0-300). We assessed whether the treatment effect was also modulated in FLEX study patients by tumor EGFR mutation status. METHODS: A tumor mutation screen of EGFR exons 18 to 21 included 971 of 1125 (86%) FLEX study patients. Treatment outcome in low and high EGFR expression groups was analyzed across efficacy endpoints according to tumor EGFR mutation status. RESULTS: Mutations in EGFR exons 18 to 21 were detected in 133 of 971 tumors (14%), 970 of which were also evaluable for EGFR expression level. The most common mutations were exon 19 deletions and L858R (124 of 133 patients; 93%). In the high EGFR expression group (immunohistochemistry score of >/=200), a survival benefit for the addition of cetuximab to chemotherapy was demonstrated in patients with EGFR wild-type (including T790M mutant) tumors. Although patient numbers were small, those in the high EGFR expression group whose tumors carried EGFR mutations may also have derived a survival benefit from the addition of cetuximab to chemotherapy. Response data suggested a cetuximab benefit in the high EGFR expression group regardless of EGFR mutation status. CONCLUSIONS: The survival benefit associated with the addition of cetuximab to first-line chemotherapy for advanced non-small-cell lung cancer expressing high levels of EGFR is not limited by EGFR mutation status.

Text recognition approaches for indoor robotics : a comparison

This paper evaluates the performance of different text recognition techniques for a mobile robot in an indoor (university campus) environment. We compared four different methods: our own approach using existing text detection methods (Minimally Stable Extremal Regions detector and Stroke Width Transform) combined with a convolutional neural network, two modes of the open source program Tesseract, and the experimental mobile app Google Goggles. The results show that a convolutional neural network combined with the Stroke Width Transform gives the best performance in correctly matched text on images with single characters whereas Google Goggles gives the best performance on images with multiple words. The dataset used for this work is released as well.

Machine learning for activity recognition : hip versus wrist data

Problem addressed Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%); acceptable classification accuracy for lying down (88.3%) and basketball (81.9%); and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%); acceptable classification accuracy for basketball (86.0%); and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

Investigating the mediating effects of emotional intelligence and coping on problem behaviours in adolescents

This study explored the mediating effect of emotional intelligence (EI) and coping strategies on problem behaviours in Australian adolescents. One hundred and forty-five adolescents (60 boys and 85 girls with a mean age of 12.02 years) completed self-report instruments of EI, stress coping strategies, and problem behaviours. The relationships between Emotional Management and Control and engagement in internalising and externalising behaviours were found to be mediated by the use of non-productive coping strategies. Mediation models of the relationship between problem behaviours and the Understanding Emotions and Emotional Recognition and Expression dimensions were found to be only partially mediated by the engagement in problem-focused and non-productive coping strategies. The results are discussed in regards to how coping strategies utilised in adolescence may produce more or less adaptive patterns of coping during adulthood. The development of emotional abilities may be required to improve coping outcomes for adolescents, which in turn may produce better psychological outcomes for adolescents in the long term.

Development of a transient, high-level expression platform for protein production in plants

Plants are an attractive alternative to conventional expression systems for the production of recombinant proteins and useful biologics, however, the economic viability of plant made proteins is strongly yield dependent. This study aimed to improve transgene expression levels in the plant host Nicotiana benthamiana using the Agroinfiltration transient expression platform. Independent investigation of the physical, chemical and genetic features associated with Agroinfiltration identified factors that improved transformation frequencies, elevated transgene expression levels and ultimately improved protein yield. The major outcome of this research was a novel hyper-expression system for biofarming recombinant proteins in plants.

Updates in inducible transgene expression using viral vectors : from transient to stable expression

The prospect of economically producing useful biologics in plants has greatly increased with the advent of viral vectors. The ability of viral vectors to amplify transgene expression has seen them develop into robust transient platforms for the high-level, rapid production of recombinant proteins. To adapt these systems to stably transformed plants, new ways of deconstructing the virus machinery and linking its expression and replication to chemically controlled promoters have been developed. The more advanced of these stable, inducible hyper-expression vectors provide both activated and amplified heterologous transgene expression. Such systems could be deployed in broad acre crops and provide a pathway to fully exploit the advantages of plants as a platform for the manufacture of a wide spectrum of products.

Towards Vision-Based Pose- and Condition-Invariant Place Recognition along Routes

Vision-based place recognition involves recognising familiar places despite changes in environmental conditions or camera viewpoint (pose). Existing training-free methods exhibit excellent invariance to either of these challenges, but not both simultaneously. In this paper, we present a technique for condition-invariant place recognition across large lateral platform pose variance for vehicles or robots travelling along routes. Our approach combines sideways facing cameras with a new multi-scale image comparison technique that generates synthetic views for input into the condition-invariant Sequence Matching Across Route Traversals (SMART) algorithm. We evaluate the system’s performance on multi-lane roads in two different environments across day-night cycles. In the extreme case of day-night place recognition across the entire width of a four-lane-plus-median-strip highway, we demonstrate performance of up to 44% recall at 100% precision, where current state-of-the-art fails.

Visual place recognition for persistent robot navigation in changing environments

This thesis demonstrates that robots can learn about how the world changes, and can use this information to recognise where they are, even when the appearance of the environment has changed a great deal. The ability to localise in highly dynamic environments using vision only is a key tool for achieving long-term, autonomous navigation in unstructured outdoor environments. The proposed learning algorithms are designed to be unsupervised, and can be generated by the robot online in response to its observations of the world, without requiring information from a human operator or other external source.

Unsupervised online learning of condition-invariant images for place recognition

This paper presents an online, unsupervised training algorithm enabling vision-based place recognition across a wide range of changing environmental conditions such as those caused by weather, seasons, and day-night cycles. The technique applies principal component analysis to distinguish between aspects of a location’s appearance that are condition-dependent and those that are condition-invariant. Removing the dimensions associated with environmental conditions produces condition-invariant images that can be used by appearance-based place recognition methods. This approach has a unique benefit – it requires training images from only one type of environmental condition, unlike existing data-driven methods that require training images with labelled frame correspondences from two or more environmental conditions. The method is applied to two benchmark variable condition datasets. Performance is equivalent or superior to the current state of the art despite the lesser training requirements, and is demonstrated to generalise to previously unseen locations.

Convolutional Neural Network-based place recognition

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.