Analysis of lower limb internal kinetics and electromyography in elite race walking.

The aim of this study was to analyse lower limb joint moments, powers and electromyography patterns in elite race walking. Twenty international male and female race walkers performed at their competitive pace in a laboratory setting. The collection of ground reaction forces (1000 Hz) was synchronised with two-dimensional high-speed videography (100 Hz) and electromyography of seven lower limb muscles (1000 Hz). As well as measuring key performance variables such as speed and stride length, normalised joint moments and powers were calculated. The rule in race walking which requires the knee to be extended from initial contact to midstance effectively made the knee redundant during stance with regard to energy generation. Instead, the leg functioned as a rigid lever which affected the role of the hip and ankle joints. The main contributors to energy generation were the hip extensors during late swing and early stance, and the ankle plantarflexors during late stance. The restricted functioning of the knee during stance meant that the importance of the swing leg in contributing to forward momentum was increased. The knee flexors underwent a phase of great energy absorption during the swing phase and this could increase the risk of injury to the hamstring muscles

Loss of miR-204 expression is a key event in melanoma

Cutaneous melanoma (CM) is a malignancy with increasing occurrence. Its microRNA repertoire has been defined in a number studies, leading to candidates for biological and clinical relevance: miR-200a/b/c, miR-203, miR-205, miR-204, miR-211, miR-23b and miR-26a/b. Our work was aimed to validate the role of these candidate miRNAs in melanoma, using additional patients cohorts and in vitro cultures. miR-26a, miR-204 and miR-211 were more expressed in normal melanocytes, while miR-23b, miR-200b/c, miR-203 and miR-205 in epidermis and keratinocytes. None of the keratinocyte-related miRNAs was associated with any known mutation or with clinical covariates in melanoma. On the other hand, the loss of miR-204 was enriched in melanomas with NRAS sole mutation (Fisher exact test, P = 0.001, Log Odds = 1.67), and less frequent than expected in those harbouring CDKN2A mutations (Fisher exact test, P = 0.001, Log Odds − 1.09). Additionally, miR-204 was associated with better prognosis in two independent melanoma cohorts and its exogenous expression led to growth impairment in melanoma cell lines. Thus, miR-204 represents a relevant mechanism in melanoma, with potential prognostic value and its loss seems to act in the CDKN2A pathway, in cooperation with NRAS

European consensus meeting of ARM-Net members concerning diagnosis and early management of newborns with anorectal malformations.

The ARM-Net (anorectal malformation network) consortium held a consensus meeting in which the classification of ARM and preoperative workup were evaluated with the aim of improving monitoring of treatment and outcome. The Krickenbeck classification of ARM and preoperative workup suggested by Levitt and Peña, used as a template, were discussed, and a collaborative consensus was achieved. The Krickenbeck classification is appropriate in describing ARM for clinical use. The preoperative workup was slightly modified. In males with a visible fistula, no cross-table lateral X-ray is needed and an anoplasty or (mini-) posterior sagittal anorectoplasty can directly be performed. In females with a small vestibular fistula (Hegar size 5 mm, and in the meantime, gentle painless dilatations can be performed. In both male and female perineal fistula and either a low birth weight (<2,000 g) or severe associated congenital anomalies, prolonged preoperative painless dilatations might be indicated to decrease perioperative morbidity caused by general anesthesia. The Krickenbeck classification is appropriate in describing ARM for clinical use. Some minor modifications to the preoperative workup by Levitt and Peña have been introduced in order to refine terminology and establish a comprehensive preoperative workup

Risk register and risk intelligence: the challenge of operational risks in the energy sector

This paper presents the needs and the challenges encountered in developing a company-wide risk register in the energy sector. The study presented comes from an electricity generation company and it was useful to indicate areas where the concept of risk registers could be extended to make better use of existing data and to support continuous improvement of risk management. Six key areas are discussed 1) aggregation of risks across the business, 2) supporting controls over mitigation measures, 3) improved estimation of event likelihood, 4) integrating with critical asset registers, 5) improving risk communication, and 6) linking with day-to-day operational practice. The paper concludes with a framework for placing risk registers at the heart of Process Safety

Green Finance: Leveraging Investment for Environmental Protection

Some political narratives describe the relationship between environmental protection and economic growth as two inherently incompatible goals. As the global community turns its attention to implementing international climate agreements, this story is ceding ground to the realization that the economy must facilitate a transition to sustainability. With limited government funding available, private investments offer an opportunity to dramatically increase and leverage funding to address daunting environmental problems. Green financing will play a critical role in the shift to a green economy. Governments, intergovernmental organizations, financial institutions, corporations, and nongovernmental organizations (NGOs) are examining green financing mechanisms in earnest. Financial institutions are enabling investment in green infrastructure, and many have signed on to the Equator Principles, a risk management framework for determining, assessing, and managing environmental and social risk in projects. NGOs and governments are promoting public policies that encourage investments in sustainability, and developing public and private mechanisms to facilitate investments in environmentally beneficial projects, such as the Paris Climate Agreement\u27s Green Climate Fund. With targets including pollution control, biodiversity protection, and materials management, as well as investments directly related to decreasing reliance on fossil fuels, the impacts of green financing could reshape the landscape for environmental professions. On June 6, 2017, ELI held a public seminar to present recent developments in this field. Below we present a transcript of the discussion, which has been edited for style, clarity, and space considerations

Mannose and xylose cannot be used as selectable agents for Vitis vinifera L. transformation

Only a few selectable marker systems for genetic engineering of grapevine have been studied in the past and only resistance to antibiotics has been used to recover transgenic vines. Since the acceptance of antibiotic resistances is small mannose and xylose were evaluated as selectable agents for the genetic transformation of grapevine. Survival of suspension cells and the ability to develop embryos from embryogenic calli were estimated in the presence of mannose and xylose. Embryogenic calli obtained from anther cultures of Vitis vinifera cv. Chardonnay were able to grow and to produce embryos even if mannose or xylose were the only source of carbohydrates

Cost Benefit Evaluation of Maintenance Options for Aging Equipment Using Monetised Risk Values: A practical application

With constant pressure to reduce maintenance costs as well as short-term budget constraints in a changing market environment, asset managers are compelled to continue operating aging assets while deferring maintenance and investment. The scope of the paper is to get an overview of the methods used to evaluate risks and opportunities for deferred maintenance interventions on aging equipment, and underline the importance to include monetised risk considerations and timeline considerations, to evaluate different scenarios connected with the possible options. Monetised risk values offer the opportunity to support risk-based decision-making using the data collected from the field. The paper presents examples of two different methods and their practical applicability in two case studies in the energy sector for a company managing power stations. The use of the existing and the new proposed solutions are discussed on the basis of their applicability to the concrete examples

Cost benefit evaluation of maintenance options for aging equipment using monetised risk values: A practical application

With constant pressure to reduce maintenance costs as well as short-term budget constraints in a changing market environment, asset managers are compelled to continue operating aging assets while deferring maintenance and investment. The scope of the paper is to get an overview of the methods used to evaluate risks and opportunities for deferred maintenance interventions on aging equipment, and underline the importance to include monetised risk considerations and timeline considerations, to evaluate different scenarios connected with the possible options. Monetised risk values offer the opportunity to support risk-based decision-making using the data collected from the field. The paper presents examples of two different methods and their practical applicability in two case studies in the energy sector for a company managing power stations. The use of the existing and the new proposed solutions are discussed on the basis of their applicability to the concrete examples

Rapid extraction of genomic DNA from small plant leaf samples

Research Not

A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm's applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained. The results demonstrate the algorithm's vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal