Global standard for the composition of infant formula: Recommendations of an ESPGHAN coordinated international expert group

The Codex Alimentarius Commission of the Food and Agriculture Organization of the United Nations (FAO) and the World Health Organization (WHO) develops food standards, guidelines and related texts for protecting consumer health and ensuring fair trade practices globally. The major part of the world's population lives in more than 160 countries that are members of the Codex Alimentarius. The Codex Standard on Infant Formula was adopted in 1981 based on scientific knowledge available in the 1970s and is currently being revised. As part of this process, the Codex Committee on Nutrition and Foods for Special Dietary Uses asked the ESPGHAN Committee on Nutrition to initiate a consultation process with the international scientific community to provide a proposal on nutrient levels in infant formulae, based on scientific analysis and taking into account existing scientific reports on the subject. ESPGHAN accepted the request and, in collaboration with its sister societies in the Federation of International Societies on Pediatric Gastroenterology, Hepatology and Nutrition, invited highly qualified experts in the area of infant nutrition to form an International Expert Group (IEG) to review the issues raised. The group arrived at recommendations on the compositional requirements for a global infant formula standard which are reported here.

An automated emergency landing system for fixed-wing aircraft: Planning and control

A number of hurdles must be overcome in order to integrate unmanned aircraft into civilian airspace for routine operations. The ability of the aircraft to land safely in an emergency is essential to reduce the risk to people, infrastructure and aircraft. To date, few field-demonstrated systems have been presented that show online re-planning and repeatability from failure to touchdown. This paper presents the development of the Guidance, Navigation and Control (GNC) component of an Automated Emergency Landing System (AELS) intended to address this gap, suited to a variety of fixed-wing aircraft. Field-tested on both a fixed-wing UAV and Cessna 172R during repeated emergency landing experiments, a trochoid-based path planner computes feasible trajectories and a simplified control system executes the required manoeuvres to guide the aircraft towards touchdown on a predefined landing site. This is achieved in zero-thrust conditions with engine forced to idle to simulate failure. During an autonomous landing, the controller uses airspeed, inertial and GPS data to track motion and maintains essential flight parameters to guarantee flyability, while the planner monitors glide ratio and re-plans to ensure approach at correct altitude. Simulations show reliability of the system in a variety of wind conditions and its repeated ability to land within the boundary of a predefined landing site. Results from field-tests for the two aircraft demonstrate the effectiveness of the proposed GNC system in live operation. Results show that the system is capable of guiding the aircraft to close proximity of a predefined keyhole in nearly 100% of cases.

The treatment of recurrent abdominal pain in children : a controlled comparison of cognitive-behavioral family intervention and standard pediatric care

This study describes the results of a controlled clinical trial involving 44 7- to 14-year-old children with recurrent abdominal pain who were randomly allocated to either cognitive-behavioral family intervention (CBFI) or standard pediatric care (SPC). Both treatment conditions resulted in significant improvements on measures of pain intensity and pain behavior. However, the children receiving CBFI had a higher rate of complete elimination of pain, lower levels of relapse at 6- and 12-month follow-up, and lower levels of interference with their activities as a result of pain and parents reported a higher level of satisfaction with the treatment than children receiving SPC. After controlling for pretreatment levels of pain, children's active self-coping and mothers' caregiving strategies were significant independent predictors of pain behavior at posttreatment.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Automated topometric graph generation from floor plan analysis

The world is rich with information such as signage and maps to assist humans to navigate. We present a method to extract topological spatial information from a generic bitmap floor plan and build a topometric graph that can be used by a mobile robot for tasks such as path planning and guided exploration. The algorithm first detects and extracts text in an image of the floor plan. Using the locations of the extracted text, flood fill is used to find the rooms and hallways. Doors are found by matching SURF features and these form the connections between rooms, which are the edges of the topological graph. Our system is able to automatically detect doors and differentiate between hallways and rooms, which is important for effective navigation. We show that our method can extract a topometric graph from a floor plan and is robust against ambiguous cases most commonly seen in floor plans including elevators and stairwells.

Standard errors and covariance matrices for smoothed rank estimators

A 'pseudo-Bayesian' interpretation of standard errors yields a natural induced smoothing of statistical estimating functions. When applied to rank estimation, the lack of smoothness which prevents standard error estimation is remedied. Efficiency and robustness are preserved, while the smoothed estimation has excellent computational properties. In particular, convergence of the iterative equation for standard error is fast, and standard error calculation becomes asymptotically a one-step procedure. This property also extends to covariance matrix calculation for rank estimates in multi-parameter problems. Examples, and some simple explanations, are given.

The standard you walk past is the standard you accept

In his 2013 three-minute speech on unacceptable behaviour in the workplace, Lieutenant General David Morrison makes the above insightful statement. While the context of the speech is the defence force, the meaning of the powerful message holds true for perioperative nursing. Perioperative nurses are part of a profession. Professions Australia, a national organisation of professional associations which aims to advance and promote professionalism for the benefit of the community, provides us with a definition of a profession2 and perioperative nurses meet this definition. Perioperative nurses possess special knowledge and skills grounded in a widely recognised body of discipline knowledge derived from research, education and high-level training. We control and regulate our own boundaries of work. We adhere to ethical and professional standards and values. Specialty professional practice standards define perioperative nurses as a community of professionals — assisting perioperative nurses when advocating for consistency in quality patient care3. It is an imperative for every professional perioperative nurse to model practice underpinned by the ACORN Standards throughout their workplace. Hence, Lieutenant General Morrison’s profound statement is a mantra each perioperative nurse could readily adopt and model in their practice environment...

Small nerve fiber quantification in the diagnosis of diabetic sensorimotor polyneuropathy: Comparing corneal confocal microscopy with intraepidermal nerve fiber density

OBJECTIVE Quantitative assessment of small fiber damage is key to the early diagnosis and assessment of progression or regression of diabetic sensorimotor polyneuropathy (DSPN). Intraepidermal nerve fiber density (IENFD) is the current gold standard, but corneal confocal microscopy (CCM), an in vivo ophthalmic imaging modality, has the potential to be a noninvasive and objective image biomarker for identifying small fiber damage. The purpose of this study was to determine the diagnostic performance of CCM and IENFD by using the current guidelines as the reference standard. RESEARCH DESIGN AND METHODS Eighty-nine subjects (26 control subjects and 63 patients with type 1 diabetes), with and without DSPN, underwent a detailed assessment of neuropathy, including CCM and skin biopsy. RESULTS Manual and automated corneal nerve fiber density (CNFD) (P < 0.0001), branch density (CNBD) (P < 0.0001) and length (CNFL) (P < 0.0001), and IENFD (P < 0.001) were significantly reduced in patients with diabetes with DSPN compared with control subjects. The area under the receiver operating characteristic curve for identifying DSPN was 0.82 for manual CNFD, 0.80 for automated CNFD, and 0.66 for IENFD, which did not differ significantly (P = 0.14). CONCLUSIONS This study shows comparable diagnostic efficiency between CCM and IENFD, providing further support for the clinical utility of CCM as a surrogate end point for DSPN.

Quality of life after early enteral feeding versus standard care for proven or suspected advanced epithelial ovarian cancer: Results from a randomised trial

Background Malnutrition is common in patients with advanced epithelial ovarian cancer (EOC), and is associated with impaired quality of life (QoL), longer hospital stay and higher risk of treatment-related adverse events. This phase III multi-centre randomised clinical trial tested early enteral feeding versus standard care on postoperative QoL. Methods From 2009 to 2013, 109 patients requiring surgery for suspected advanced EOC, moderately to severely malnourished were enrolled at five sites across Queensland and randomised to intervention (n = 53) or control (n = 56) groups. Intervention involved intraoperative nasojejunal tube placement and enteral feeding until adequate oral intake could be maintained. Despite being randomised to intervention, 20 patients did not receive feeds (13 did not receive the feeding tube; 7 had it removed early). Control involved postoperative diet as tolerated. QoL was measured at baseline, 6 weeks postoperatively and 30 days after the third cycle of chemotherapy. The primary outcome measure was the difference in QoL between the intervention and the control group. Secondary endpoints included treatment-related adverse event occurrence, length of stay, postoperative services use, and nutritional status. Results Baseline characteristics were comparable between treatment groups. No significant difference in QoL was found between the groups at any time point. There was a trend towards better nutritional status in patients who received the intervention but the differences did not reach statistical significance except for the intention-to-treat analysis at 7 days postoperatively (11.8 intervention vs. 13.8 control, p 0.04). Conclusion Early enteral feeding did not significantly improve patients' QoL compared to standard of care but may improve nutritional status.

Dasatinib, nilotinib and standard-dose imatinib for the first line treatment of chronic myeloid leukaemia: Systematic reviews and economic analyses

- Background Nilotinib and dasatinib are now being considered as alternative treatments to imatinib as a first-line treatment of chronic myeloid leukaemia (CML). - Objective This technology assessment reviews the available evidence for the clinical effectiveness and cost-effectiveness of dasatinib, nilotinib and standard-dose imatinib for the first-line treatment of Philadelphia chromosome-positive CML. - Data sources Databases [including MEDLINE (Ovid), EMBASE, Current Controlled Trials, ClinicalTrials.gov, the US Food and Drug Administration website and the European Medicines Agency website] were searched from search end date of the last technology appraisal report on this topic in October 2002 to September 2011. - Review methods A systematic review of clinical effectiveness and cost-effectiveness studies; a review of surrogate relationships with survival; a review and critique of manufacturer submissions; and a model-based economic analysis. - Results Two clinical trials (dasatinib vs imatinib and nilotinib vs imatinib) were included in the effectiveness review. Survival was not significantly different for dasatinib or nilotinib compared with imatinib with the 24-month follow-up data available. The rates of complete cytogenetic response (CCyR) and major molecular response (MMR) were higher for patients receiving dasatinib than for those with imatinib for 12 months' follow-up (CCyR 83% vs 72%, p < 0.001; MMR 46% vs 28%, p < 0.0001). The rates of CCyR and MMR were higher for patients receiving nilotinib than for those receiving imatinib for 12 months' follow-up (CCyR 80% vs 65%, p < 0.001; MMR 44% vs 22%, p < 0.0001). An indirect comparison analysis showed no difference between dasatinib and nilotinib for CCyR or MMR rates for 12 months' follow-up (CCyR, odds ratio 1.09, 95% CI 0.61 to 1.92; MMR, odds ratio 1.28, 95% CI 0.77 to 2.16). There is observational association evidence from imatinib studies supporting the use of CCyR and MMR at 12 months as surrogates for overall all-cause survival and progression-free survival in patients with CML in chronic phase. In the cost-effectiveness modelling scenario, analyses were provided to reflect the extensive structural uncertainty and different approaches to estimating OS. First-line dasatinib is predicted to provide very poor value for money compared with first-line imatinib, with deterministic incremental cost-effectiveness ratios (ICERs) of between £256,000 and £450,000 per quality-adjusted life-year (QALY). Conversely, first-line nilotinib provided favourable ICERs at the willingness-to-pay threshold of £20,000-30,000 per QALY. - Limitations Immaturity of empirical trial data relative to life expectancy, forcing either reliance on surrogate relationships or cumulative survival/treatment duration assumptions. - Conclusions From the two trials available, dasatinib and nilotinib have a statistically significant advantage compared with imatinib as measured by MMR or CCyR. Taking into account the treatment pathways for patients with CML, i.e. assuming the use of second-line nilotinib, first-line nilotinib appears to be more cost-effective than first-line imatinib. Dasatinib was not cost-effective if decision thresholds of £20,000 per QALY or £30,000 per QALY were used, compared with imatinib and nilotinib. Uncertainty in the cost-effectiveness analysis would be substantially reduced with better and more UK-specific data on the incidence and cost of stem cell transplantation in patients with chronic CML. - Funding The Health Technology Assessment Programme of the National Institute for Health Research.

Towards automated content analysis of discussion transcripts: A cognitive presence case

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.

Designing against boredom in automated driving

I draw on four years of experience in mobility and transport research. I was part of a research project with Siemens, for which we identified global trends in urban mobility and explored future business opportunities through scenario planning methods. Some of the proposed solutions for personal and public transport included driverless vehicles. In collaboration with BMW Design I explored the potential of new materials for automotive user interfaces...

Infrared thermal imaging for automated detection of diabetic foot complications

Background Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability of high-resolution infrared thermal imaging for noninvasive automated detection of signs of diabetic foot disease. Methods The plantar foot surfaces of 15 diabetes patients were imaged with an infrared camera (resolution, 1.2 mm/pixel): 5 patients had no visible signs of foot complications, 5 patients had local complications (e.g., abundant callus or neuropathic ulcer), and 5 patients had difuse complications (e.g., Charcot foot, infected ulcer, or critical ischemia). Foot temperature was calculated as mean temperature across pixels for the whole foot and for specified regions of interest (ROIs). Results No diferences in mean temperature >1.5 °C between the ipsilateral and the contralateral foot were found in patients without complications. In patients with local complications, mean temperatures of the ipsilateral and the contralateral foot were similar, but temperature at the ROI was >2 °C higher compared with the corresponding region in the contralateral foot and to the mean of the whole ipsilateral foot. In patients with difuse complications, mean temperature diferences of >3 °C between ipsilateral and contralateral foot were found. Conclusions With an algorithm based on parameters that can be captured and analyzed with a high-resolution infrared camera and a computer, it is possible to detect signs of diabetic foot disease and to discriminate between no, local, or difuse diabetic foot complications. As such, an intelligent telemedicine monitoring system for noninvasive automated detection of signs of diabetic foot disease is one step closer. Future studies are essential to confirm and extend these promising early findings.

Visual detection of occluded crop: For automated harvesting

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.