Comparison of single and multi-analyte methods based on LC-MS/MS for mycotoxin biomarker determination in human urine

The performances of four LC-MS/MS methodologies for determination of up to eight mycotoxin biomarkers in human urines were compared by involving three laboratories that analysed common urine samples spiked at two levels of each biomarker. Each laboratory received a calibration solution, spiked urines and the corresponding unspiked urine. The two spiking levels for each biomarker were chosen by considering the levels naturally occurring in human urines and the limits of quantification of the LC-MS/MS methodologies used by the participating laboratories. The results of each laboratory were evaluated for their z-score values. The percentage of satisfactory z-scores (vertical bar z vertical bar 2) were obtained for fumonisin B-1 (7/12 results), ochratoxin A (4/8 results) and alpha-zearalenol (1/8 results). The percentage of satisfactory z-scores for fumonisin B-1 and ochratoxin A increased from 42 to 83% for fumonisin B-1 and from 50 to 62% for ochratoxin A when laboratories 1 and 2 used own calibrants. Factors that could explain the different results obtained for fumonisin B-1 and ochratoxin A with provided and own calibration solutions could not be identified in this study and should be carefully investigated in future studies.

A research-based approach to exploring the adoption of novel UK-based concrete test methods in Chinese construction sector

As part of a UK-China science bridge project - a UK government funded initiative linking leading universities and businesses in selective partnering countries in 2009 a collaborative research programme was initiated between Queen's University and the Research Institute of High Performance Concrete (part of the Central Research Institute of Building and Construction) in Beijing.

For further details email b.magee@ulster.ac.uk

Solubility parameter based screening methods for early stage formulation development of itraconazole amorphous solid dispersions

Objectives: This article uses conventional and newly extended solubility parameter (δ) methods to identify polymeric materials capable of forming amorphous dispersions with itraconazole (itz). Methods: Combinations of itz and Soluplus, Eudragit E PO (EPO), Kollidon 17PF (17PF) or Kollidon VA64 (VA64) were prepared as amorphous solid dispersions using quench cooling and hot melt extrusion. Storage stability was evaluated under a range of conditions using differential scanning calorimetry and powder X-ray diffraction. Key findings: The rank order of itz miscibility with polymers using both conventional and novel δ-based approaches was 17PF > VA64 > Soluplus > EPO, and the application of the Flory–Huggins lattice model to itz–excipient binary systems corroborated the findings. The solid-state characterisation analyses of the formulations manufactured by melt extrusion correlated well with pre-formulation screening. Long-term storage studies showed that the physical stability of 17PF/vitamin E TPGS–itz was poor compared with Soluplus and VA64 formulations, and for EPO/itz systems variation in stability may be observed depending on the preparation method. Conclusion: Results have demonstrated that although δ-based screening may be useful in predicting the initial state of amorphous solid dispersions, assessment of the physical behaviour of the formulations at relevant temperatures may be more appropriate for the successful development of commercially acceptable amorphous drug products.

A simplified procedure for sizing solar thermal systems based on national assessment methods in the UK and Ireland.

Solar heating systems have the potential to be an efficient renewable energy technology, provided they are sized correctly. Sizing a solar thermal system for domestic applications does not warrant the cost of a simulation. As a result simplified sizing procedures are required. The size of a system depends on a number of variables including the efficiency of the collector itself, the hot water demand and the solar radiation at a given location. Domestic Hot Water (DHW) demand varies with time and is assessed using a multi-parameter detailed model. Secondly, the national energy evaluation methodologies are evaluated from the perspective of solar thermal system sizing. Based on the assessment of the standards, limitations in the evaluation method for solar thermal systems are outlined and an adapted method, specific to the sizing of solar thermal systems, is proposed. The methodology is presented for two common dwelling scenarios. Results from this showed that it is difficult to achieve a high solar fraction given practical sizes of system infrastructure (storage tanks) for standard domestic properties. However, solar thermal systems can significantly offset energy loads due associated DHW consumption, particularly when sized appropriately. The presented methodology is valuable for simple solar system design and also for the quick comparison of salient criteria.

Recurrent Convolutional Network for Video-based Person Re-Identification

In this paper we propose a novel recurrent neural networkarchitecture for video-based person re-identification.Given the video sequence of a person, features are extracted from each frame using a convolutional neural network that incorporates a recurrent final layer, which allows information to flow between time-steps. The features from all time steps are then combined using temporal pooling to give an overall appearance feature for the complete sequence. The convolutional network, recurrent layer, and temporal pooling layer, are jointly trained to act as a feature extractor for video-based re-identification using a Siamese network architecture.Our approach makes use of colour and optical flow information in order to capture appearance and motion information which is useful for video re-identification. Experiments are conduced on the iLIDS-VID and PRID-2011 datasets to show that this approach outperforms existing methods of video-based re-identification.

https://github.com/niallmcl/Recurrent-Convolutional-Video-ReID
Project Source Code

Impact of an evidence-based online module on best practice in physical therapy for children with developmental coordination disorder: a mixed methods study

Aims : This study evaluates the impact of a Developmental Coordination Disorder (DCD) evidence-based online module including synthesized resources, practical strategies, and interactive component on self-reported physical therapist (PT) knowledge, skills, and practice. Methods : PTs from across Canada completed questionnaires before, immediately after, and 2 months following completion of the module. Questionnaires used 7-point Likert scale items and short open-ended questions; analyzes used paired t-tests and a thematic approach. Results : Fifty PTs completed both pre- and post-questionnaires; 41 of these completed the follow-up questionnaire. Most items (79%) evaluating self-reported knowledge and skills increased significantly following module completion and this increase was maintained two months later. Most participants (92%) reported an increase in their confidence to provide DCD evidence-based services. Participants plan to modify their evaluative practices (e.g., involving children in goal setting) and their management of DCD (e.g., using best practice principles, providing resources to families and physicians). At the 2- month follow-up, 46% of participants had returned to the module to review information (e.g., video, resources) or to download handouts. Conclusion : An online module developed collaboratively with PTs has the potential not only to increase PTs’ knowledge, but also to support them in implementing evidence-based services for children with DCD.

Face recogniton based on the optimal combination of neural networks, eigenfaces and least squares matching methods

Report of a research project of the Fachhochschule Hannover, University of Applied Sciences and Arts, Department of Information Technologies. Automatic face recognition increases the security standards at public places and border checkpoints. The picture inside the identification documents could widely differ from the face, that is scanned under random lighting conditions and for unknown poses. The paper describes an optimal combination of three key algorithms of object recognition, that are able to perform in real time. The camera scan is processed by a recurrent neural network, by a Eigenfaces (PCA) method and by a least squares matching algorithm. Several examples demonstrate the achieved robustness and high recognition rate.

Damage evaluation of drilled carbon/epoxy laminates based on area assessment methods

The characteristics of carbon fibre reinforced laminates had widened their use, from aerospace to domestic appliances. A common characteristic is the need of drilling for assembly purposes. It is known that a drilling process that reduces the drill thrust force can decrease the risk of delamination. In this work, delamination assessment methods based on radiographic data are compared and correlated with mechanical test results (bearing test).

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Statistical model based 3D shape prediction of postoperative trunks for non-invasive scoliosis surgery planning

One of the major concerns of scoliosis patients undergoing surgical treatment is the aesthetic aspect of the surgery outcome. It would be useful to predict the postoperative appearance of the patient trunk in the course of a surgery planning process in order to take into account the expectations of the patient. In this paper, we propose to use least squares support vector regression for the prediction of the postoperative trunk 3D shape after spine surgery for adolescent idiopathic scoliosis. Five dimensionality reduction techniques used in conjunction with the support vector machine are compared. The methods are evaluated in terms of their accuracy, based on the leave-one-out cross-validation performed on a database of 141 cases. The results indicate that the 3D shape predictions using a dimensionality reduction obtained by simultaneous decomposition of the predictors and response variables have the best accuracy.

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

Case based reasoning as an extension of fault dictionary methods for linear electronic analog circuits diagnosis

El test de circuits és una fase del procés de producció que cada vegada pren més importància quan es desenvolupa un nou producte. Les tècniques de test i diagnosi per a circuits digitals han estat desenvolupades i automatitzades amb èxit, mentre que aquest no és encara el cas dels circuits analògics. D'entre tots els mètodes proposats per diagnosticar circuits analògics els més utilitzats són els diccionaris de falles. En aquesta tesi se'n descriuen alguns, tot analitzant-ne els seus avantatges i inconvenients. Durant aquests últims anys, les tècniques d'Intel·ligència Artificial han esdevingut un dels camps de recerca més importants per a la diagnosi de falles. Aquesta tesi desenvolupa dues d'aquestes tècniques per tal de cobrir algunes de les mancances que presenten els diccionaris de falles. La primera proposta es basa en construir un sistema fuzzy com a eina per identificar. Els resultats obtinguts son força bons, ja que s'aconsegueix localitzar la falla en un elevat tant percent dels casos. Per altra banda, el percentatge d'encerts no és prou bo quan a més a més s'intenta esbrinar la desviació. Com que els diccionaris de falles es poden veure com una aproximació simplificada al Raonament Basat en Casos (CBR), la segona proposta fa una extensió dels diccionaris de falles cap a un sistema CBR. El propòsit no és donar una solució general del problema sinó contribuir amb una nova metodologia. Aquesta consisteix en millorar la diagnosis dels diccionaris de falles mitjançant l'addició i l'adaptació dels nous casos per tal d'esdevenir un sistema de Raonament Basat en Casos. Es descriu l'estructura de la base de casos així com les tasques d'extracció, de reutilització, de revisió i de retenció, fent èmfasi al procés d'aprenentatge. En el transcurs del text s'utilitzen diversos circuits per mostrar exemples dels mètodes de test descrits, però en particular el filtre biquadràtic és l'utilitzat per provar les metodologies plantejades, ja que és un dels benchmarks proposats en el context dels circuits analògics. Les falles considerades son paramètriques, permanents, independents i simples, encara que la metodologia pot ser fàcilment extrapolable per a la diagnosi de falles múltiples i catastròfiques. El mètode es centra en el test dels components passius, encara que també es podria extendre per a falles en els actius.

Sensitivity of feature-based analysis methods of storm tracks to the form of background field removal

For the tracking of extrema associated with weather systems to be applied to a broad range of fields it is necessary to remove a background field that represents the slowly varying, large spatial scales. The sensitivity of the tracking analysis to the form of background field removed is explored for the Northern Hemisphere winter storm tracks for three contrasting fields from an integration of the U. K. Met Office's (UKMO) Hadley Centre Climate Model (HadAM3). Several methods are explored for the removal of a background field from the simple subtraction of the climatology, to the more sophisticated removal of the planetary scales. Two temporal filters are also considered in the form of a 2-6-day Lanczos filter and a 20-day high-pass Fourier filter. The analysis indicates that the simple subtraction of the climatology tends to change the nature of the systems to the extent that there is a redistribution of the systems relative to the climatological background resulting in very similar statistical distributions for both positive and negative anomalies. The optimal planetary wave filter removes total wavenumbers less than or equal to a number in the range 5-7, resulting in distributions more easily related to particular types of weather system. For the temporal filters the 2-6-day bandpass filter is found to have a detrimental impact on the individual weather systems, resulting in the storm tracks having a weak waveguide type of behavior. The 20-day high-pass temporal filter is less aggressive than the 2-6-day filter and produces results falling between those of the climatological and 2-6-day filters.