Weight changes following lower limb arthroplasty : a prospective observational study

The aim of this study was to assess patterns of weight loss/gain following total hip or knee joint replacement. Four hundred and fifty primary lower limb arthroplasty patients, where the current surgery was the last limiting factor to improved mobility, were selected. Over a one year period 212 gained weight (mean 5.03kg), 92 remained static, and 146 lost weight. The median change was a weight gain of 0.50Kg (p=0.002). All patients had a significant improvement in Oxford outcome scores. Hip arthroplasty patients were statistically more likely to gain weight than knee arthroplasty patients. A successful arthroplasty, restoring a patient's mobility, does not necessarily lead to subsequent weight loss. The majority of patients put on weight with an overall net weight gain. No adverse effect on functional outcome was noted

Hands-on Gravitational Wave Astronomy: Extracting astrophysical information from simulated signals

In this paper we introduce a hands-on activity in which introductory astronomy students act as gravitational wave astronomers by extracting information from simulated gravitational wave signals. The process mimics the way true gravitational wave analysis will be handled by using plots of a pure gravitational wave signal. The students directly measure the properties of the simulated signal, and use these measurements to evaluate standard formulae for astrophysical source parameters. An exercise based on the discussion in this paper has been written and made publicly available online for use in introductory laboratory courses.Comment: 5 pages, 4 figures; submitted to Am. J. Phy

A survey of the treatment and management of patients with severe chronic spontaneous urticaria.

Chronic spontaneous urticaria (CSU) is characterized by the recurrent appearance of weals, angio‐oedema or both, occurring at least twice weekly for longer than 6 weeks.1 It is often managed with antihistamines, but occasionally requires other systemic agents in recalcitrant cases. A cross‐sectional survey was conducted by means of an internet‐based survey tool (Typeform; https://www.typeform.com). Participating consultants with a specialist interest in urticaria were identified through the specialist registers of the British Society of Allergy and Clinical Immunology (BSACI), the Improving Quality in Allergy Services (IQAS) Group and the British Association of Dermatologists (BAD), and invited to take part. The survey content was based on current CSU treatment guidelines from EAACI/GA2LEN/EDF/WAO1 and the British Society for Allergy and Clinical Immunology (BSACI).2 The EAACI/GA2LEN/EDF/WAO guidelines are a joint initiative of the Dermatology Section of the European Academy of Allergy and Clinical Immunology (EAACI), the Global Allergy and Asthma European Network (GA2LEN) (a European Union‐funded network of excellence), the European Dermatology Forum (EDF), and the World Allergy Organization (WAO). To standardize responses, all participants were presented with a case of recalcitrant CSU (failed on maximum dose of nonsedating antihistamines and montelukast), requiring alternative systemic treatment. Questions covered usage of systemic treatments, routine disease severity assessments, adherence to treatment guidelines and perceived barriers to prescribing. Responses (Table 1) were received from 19 UK consultants (26 surveys sent; completion rate 73%), 15 of whom had > 10 years’ experience in the treatment of CSU. The majority were allergy (58%) and dermatology consultants (37%). Of the 19 consultants, 56% provide a dedicated urticaria service, 37% treat both adult and paediatric patients, and the majority (79%) use systemic medications other than antihistamines and montelukast. Omalizumab and ciclosporin were the most commonly used first‐line agents (47% and 27% respectively) (Fig. 1). The majority (84%) of consultants use validated measures to assess disease severity, including the weekly Urticaria Activity Score (UAS‐7, 63%), the Physician Global Assessment (63%), the Patient Global Assessment (44%) and the Dermatology Quality of Life Index (DLQI) (38%). Guidelines are used by 89% to direct their management of CSU, with 50% using the EAACI/GA2LEN/EDF/WAO guideline,1 compared with 31% primarily using the BSACI guideline.2 The main perceived barriers to prescribing systemic medications were potential adverse effects (AEs) (32% strongly agreed), potential long‐term toxicity (26% strongly agreed), cost of treatment (42% strongly agreed), and views expressed by the patient and their family (37% agreed)

Exploring the challenges in developing a multi-criteria assessment for smart local energy systems

Several countries worldwide, including the United Kingdom, are investing in and introducing policies to foster the development and deployment of Smart Local Energy Systems. Smart Local Energy Systems are complex and socio-technical, with a wide range of stakeholders and multiple social, technical, environmental and economic aims. It is, therefore, essential to develop a standardised assessment tool to monitor the implementation of these systems and their social, technological, environmental and economic benefits and impacts. This paper presents work related to developing such a multi-criteria assessment tool, focusing on exploring and identifying the challenges of applying multi-criteria assessment to the development and deployment of Smart Local Energy Systems. The research involved semi-structured interviews with relevant expert stakeholders concerning six core assessment themes, corresponding sub-themes, and associated criteria/metrics. The results provide insights into the challenges of applying multi-criteria assessment to Smart Local Energy Systems and highlight the complex nature of these systems. Furthermore, stakeholder burnout (due to too many stakeholder engagement activities), data collection issues, and the broad definition and/or limited scope of assessment criteria were identified as the principal challenges faced in developing such an assessment tool, potentially affecting the reliability of its outputs

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

Effect of daily restriction and age at initiation of a skip-a-day program for young broiler breeders.

Two experiments were conducted with Cobb feather sex broiler breeders comparing skip-a-day (SAD) feeding programs which began at either 2, 4, 6 or 8 wk of age. A fifth program, daily restriction started at 2 wk of age, was also compared. Chicks hatched in December and July, respectively, in Experiments 1 and 2 were exposed to natural daylight until 20 wk of age. All birds were fed ad libitum until the respective restriction programs began. All grower programs terminated at 20 wk of age. A breeder diet was given daily after 20 wk. Males and females were grown together. Sexual maturity was reached earlier in the 2-wk restriction groups (2-wi SAD in Experiment 1 and the 2-wk daily restriction in both experiments) than in the 8-wk SAD group. Egg production in Experiment 1 was also improved by the early restriction. Fertility and hatchability were not significantly affected by treatment. Based on the results of these experiments a SAD program beginning at 2 wk of age was as good as or better than one initiated at later ages. The 2-wk daily restriction program was equivalent to the 2-wk SAD program

Lighting of end of lay broiler breeders: fluorescent versus incandescent.

An 18-week experiment was conducted to investigate the effects of changing from incandescent to fluorescent lighting on egg production, egg weight, fertility, and hatchability of end of lay broiler breeders housed in an open-sided house. Forty-eight-week-old Cobb feather-sexed broiler breeders were housed, 30 females and 3 males per pen, in a total of 28 pens. Incandescent lights had been used previously, so pens were randomly assigned to either fluorescent or incandescent lights giving 20 lx of light at bird level. Lights used were 60 W incandescent and 22 W fluorescent cool-white circular. Body weight and egg production were measured weekly, and fertility, hatchability, and egg weight were determined monthly from 48 to 65 weeks of age. No significant treatment effects were observed on body weight, fertility, hatchability, or egg weight. A significant reduction in egg production was observed with fluorescent lighting from Weeks 58 to 65. The reduced egg production indicated it was detrimental to change from incandescent to cool-white fluorescent lighting

Self-Updating Models with Error Remediation

Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not all of which is representative of the original, limited training data. However, updating these deployed models can be difficult due to logistical, bandwidth, time, hardware, and/or data sensitivity constraints. We propose a framework, Self-Updating Models with Error Remediation (SUMER), in which a deployed model updates itself as new data becomes available. SUMER uses techniques from semi-supervised learning and noise remediation to iteratively retrain a deployed model using intelligently-chosen predictions from the model as the labels for new training iterations. A key component of SUMER is the notion of error remediation as self-labeled data can be susceptible to the propagation of errors. We investigate the use of SUMER across various data sets and iterations. We find that self-updating models (SUMs) generally perform better than models that do not attempt to self-update when presented with additional previously-unseen data. This performance gap is accentuated in cases where there is only limited amounts of initial training data. We also find that the performance of SUMER is generally better than the performance of SUMs, demonstrating a benefit in applying error remediation. Consequently, SUMER can autonomously enhance the operational capabilities of existing data processing systems by intelligently updating models in dynamic environments.Comment: 17 pages, 13 figures, published in the proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II conference in the SPIE Defense + Commercial Sensing, 2020 symposiu