Prediction of landing gear loads using machine learning techniques

This article investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, the results on drop test data and flight test data will be examined. This article will focus on the use of Gaussian process regression for the prediction of loads on the components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the Gaussian process to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear using measurements that are readily available or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements are considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction

Top down tandem mass spectrometric analysis of a chemically modified rough-type lipopolysaccharide vaccine candidate

Recent advances in lipopolysaccharide (LPS) biology have led to its use in drug discovery pipelines, including vaccine and vaccine adjuvant discovery. Desirable characteristics for LPS vaccine candidates include both the ability to produce a specific antibody titer in patients and a minimal host inflammatory response directed by the innate immune system. However, in-depth chemical characterization of most LPS extracts has not been performed; hence, biological activities of these extracts are unpredictable. Additionally, the most widely adopted workflow for LPS structure elucidation includes nonspecific chemical decomposition steps before analyses, making structures inferred and not necessarily biologically relevant. In this work, several different mass spectrometry workflows that have not been previously explored were employed to show proof-of-principle for top down LPS primary structure elucidation, specifically for a rough-type mutant(J5) E.coli-derived LPS component of a vaccine candidate. First, ion mobility filtered precursor ions were subjected to collision induced dissociation (CID) to define differences in native J5 LPS v. chemically detoxified J5 LPS (dLPS). Next, ultra-high mass resolving power, accurate mass spectrometry was employed for unequivocal precursor and product ion empirical formulae generation. Finally, MS 3 analyses in an ion trap instrument showed that previous knowledge about dissociation of LPS components can be used to reconstruct and sequence LPS in a top down fashion. A structural rationale is also explained for differential inflammatory dose-response curves, in vitro, when HEK-Blue hTLR4 cells were administered increasing concentrations of native J5 LPS v. dLPS, which will be useful in future drug discovery efforts

Why is it difficult to implement e-health initiatives? A qualitative study

<b>Background</b> The use of information and communication technologies in healthcare is seen as essential for high quality and cost-effective healthcare. However, implementation of e-health initiatives has often been problematic, with many failing to demonstrate predicted benefits. This study aimed to explore and understand the experiences of implementers - the senior managers and other staff charged with implementing e-health initiatives and their assessment of factors which promote or inhibit the successful implementation, embedding, and integration of e-health initiatives.<p></p> <b>Methods</b> We used a case study methodology, using semi-structured interviews with implementers for data collection. Case studies were selected to provide a range of healthcare contexts (primary, secondary, community care), e-health initiatives, and degrees of normalization. The initiatives studied were Picture Archiving and Communication System (PACS) in secondary care, a Community Nurse Information System (CNIS) in community care, and Choose and Book (C&B) across the primary-secondary care interface. Implementers were selected to provide a range of seniority, including chief executive officers, middle managers, and staff with 'on the ground' experience. Interview data were analyzed using a framework derived from Normalization Process Theory (NPT).<p></p> <b>Results</b> Twenty-three interviews were completed across the three case studies. There were wide differences in experiences of implementation and embedding across these case studies; these differences were well explained by collective action components of NPT. New technology was most likely to 'normalize' where implementers perceived that it had a positive impact on interactions between professionals and patients and between different professional groups, and fit well with the organisational goals and skill sets of existing staff. However, where implementers perceived problems in one or more of these areas, they also perceived a lower level of normalization.<p></p> <b>Conclusions</b> Implementers had rich understandings of barriers and facilitators to successful implementation of e-health initiatives, and their views should continue to be sought in future research. NPT can be used to explain observed variations in implementation processes, and may be useful in drawing planners' attention to potential problems with a view to addressing them during implementation planning

Workgroup Report: Drinking-Water Nitrate and Health—Recent Findings and Research Needs

Human alteration of the nitrogen cycle has resulted in steadily accumulating nitrate in our water resources. The U.S. maximum contaminant level and World Health Organization guidelines for nitrate in drinking water were promulgated to protect infants from developing methemoglobinemia, an acute condition. Some scientists have recently suggested that the regulatory limit for nitrate is overly conservative; however, they have not thoroughly considered chronic health outcomes. In August 2004, a symposium on drinking-water nitrate and health was held at the International Society for Environmental Epidemiology meeting to evaluate nitrate exposures and associated health effects in relation to the current regulatory limit. The contribution of drinking-water nitrate toward endogenous formation of N-nitroso compounds was evaluated with a focus toward identifying subpopulations with increased rates of nitrosation. Adverse health effects may be the result of a complex interaction of the amount of nitrate ingested, the concomitant ingestion of nitrosation cofactors and precursors, and specific medical conditions that increase nitrosation. Workshop participants concluded that more experimental studies are needed and that a particularly fruitful approach may be to conduct epidemiologic studies among susceptible subgroups with increased endogenous nitrosation. The few epidemiologic studies that have evaluated intake of nitrosation precursors and/or nitrosation inhibitors have observed elevated risks for colon cancer and neural tube defects associated with drinking-water nitrate concentrations below the regulatory limit. The role of drinking-water nitrate exposure as a risk factor for specific cancers, reproductive outcomes, and other chronic health effects must be studied more thoroughly before changes to the regulatory level for nitrate in drinking water can be considered

Computer-assisted and fractal-based morphometric assessment of microvascularity in histological specimens of gliomas

Fractal analysis is widely applied to investigate the vascular system in physiological as well as pathological states. We propose and examine a computer-aided and fractal-based image analysis technique to quantify the microvascularity in histological specimens of WHO grade II and III gliomas. A computer-aided and fractal-based analysis was used to describe the microvessels and to quantify their geometrical complexity in histological specimens collected from 17 patients. The statistical analysis showed that the fractal-based indexes are the most discriminant parameters to describe the microvessels. The computer-aided quantitative analysis also showed that grade III gliomas are generally more vascularized than grade II gliomas. The fractal parameters are reliable quantitative indicators of the neoplastic microvasculature, making them potential surrogate biomarkers. The qualitative evaluation currently performed by the neuropathologist can be combined with the computer-assisted quantitative analysis of the microvascularity to improve the diagnosis and optimize the treatment of patients with brain cancer

Performance of Proximity Loggers in Recording Intra- and Inter-Species Interactions: A Laboratory and Field-Based Validation Study

Knowledge of the way in which animals interact through social networks can help to address questions surrounding the ecological and evolutionary consequences of social organisation, and to understand and manage the spread of infectious diseases. Automated proximity loggers are increasingly being used to record interactions between animals, but the accuracy and reliability of the collected data remain largely un-assessed. Here we use laboratory and observational field data to assess the performance of these devices fitted to a herd of 32 beef cattle (Bos taurus) and nine groups of badgers (Meles meles, n  = 77) living in the surrounding woods. The distances at which loggers detected each other were found to decrease over time, potentially related to diminishing battery power that may be a function of temperature. Loggers were highly accurate in recording the identification of contacted conspecifics, but less reliable at determining contact duration. There was a tendency for extended interactions to be recorded as a series of shorter contacts. We show how data can be manipulated to correct this discrepancy and accurately reflect observed interaction patterns by combining records between any two loggers that occur within a 1 to 2 minute amalgamation window, and then removing any remaining 1 second records. We make universally applicable recommendations for the effective use of proximity loggers, to improve the validity of data arising from future studies

Isothiocyanates are detected in human synovial fluid following broccoli consumption and can affect the tissues of the knee joint

Osteoarthritis is a major cause of disability and there is no current pharmaceutical treatment which can prevent the disease or slow its progression. Dietary advice or supplementation is clearly an attractive option since it has low toxicity and ease of implementation on a population level. We have previously demonstrated that sulforaphane, a dietary isothiocyanate derived from its glucosinolate precursor which is found in broccoli, can prevent cartilage destruction in cells, in in vitro and in vivo models of osteoarthritis. As the next phase of this research, we enrolled 40 patients with knee osteoarthritis undergoing total knee replacement into a proof-of-principle trial. Patients were randomised to either a low or high glucosinolate diet for 14 days prior to surgery. We detected ITCs in the synovial fluid of the high glucosinolate group, but not the low glucosinolate group. This was mirrored by an increase in ITCs and specifically sulforaphane in the plasma. Proteomic analysis of synovial fluid showed significantly distinct profiles between groups with 125 differentially expressed proteins. The functional consequence of this diet will now be tested in a clinical trial

A review and meta-analysis of prospective studies of red and processed meat intake and prostate cancer

Over the past decade, several large epidemiologic investigations of meat intake and prostate cancer have been published. Therefore, a meta-analysis of prospective studies was conducted to estimate potential associations between red or processed meat intake and prostate cancer. Fifteen studies of red meat and 11 studies of processed meat were included in the analyses. High vs. low intake and dose-response analyses were conducted using random effects models to generate summary relative risk estimates (SRRE). No association between high vs. low red meat consumption (SRRE = 1.00, 95% CI: 0.96-1.05) or each 100 g increment of red meat (SRRE = 1.00, 95% CI: 0.95-1.05) and total prostate cancer was observed. Similarly, no association with red meat was observed for advanced prostate cancer (SRRE = 1.01, 95% CI: 0.94-1.09). A weakly elevated summary association between processed meat and total prostate cancer was found (SRRE = 1.05, 95% CI: 0.99-1.12), although heterogeneity was present, the association was attenuated in a sub-group analysis of studies that adjusted for multiple potential confounding factors, and publication bias likely affected the summary effect. In conclusion, the results of this meta-analysis are not supportive of an independent positive association between red or processed meat intake and prostate cancer

The Vista Variables in the Via Lactea (VVV) ESO Public Survey: Current Status and First Results

25 pages, 18 figures. To appear in the Carnegie Observatories Astrophysics Series, Volume 525 pages, 18 figures. To appear in the Carnegie Observatories Astrophysics Series, Volume 5Vista Variables in the Via Lactea (VVV) is an ESO Public Survey that is performing a variability survey of the Galactic bulge and part of the inner disk using ESO's Visible and Infrared Survey Telescope for Astronomy (VISTA). The survey covers 520 deg^2 of sky area in the ZYJHK_S filters, for a total observing time of 1929 hours, including ~ 10^9 point sources and an estimated ~ 10^6 variable stars. Here we describe the current status of the VVV Survey, in addition to a variety of new results based on VVV data, including light curves for variable stars, newly discovered globular clusters, open clusters, and associations. A set of reddening-free indices based on the ZYJHK_S system is also introduced. Finally, we provide an overview of the VVV Templates Project, whose main goal is to derive well-defined light curve templates in the near-IR, for the automated classification of VVV light curves