Determination of the ethylene oxide adduct S-(2-hydroxyethyl)cysteine by a fluorometric HPLC method in albumin and globin from human blood

A new method has been developed for the quantification of 2-hydroxyethylated cysteine resulting as adduct in blood proteins after human exposure to ethylene oxide, by reversed-phase HPLC with fluorometric detection. The specific adduct is analysed in albumin and in globin. After isolation of albumin and globin from blood, acid hydrolysis of the protein and precolumn derivatisation of the digest with 9-fluorenylmethoxycarbonylchloride, the levels of derivatised S-hydroxyethylcysteine are analysed by RP-HPLC and fluorescence detection, with a detection limit of 8 nmol/g protein. Background levels of S-hydroxyethylcysteine were quantified in both albumin and globin, under special consideration of the glutathione transferase GSTT1 and GSTM1 polymorphisms. GSTT1 polymorphism had a marked influence on the physiological background alkylation of cysteine. While S-hydroxyethylcysteine levels in "non-conjugators" were between 15 and 50 nmol/g albumin, "low conjugators" displayed levels between 8 and 21 nmol/g albumin, and "high conjugators" did not show levels above the detection limit. The human GSTM1 polymorphism had no apparent effect on background levels of blood protein 2-hydroxyethylation.

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Colloidal drug probe : Method development and validation for adhesion force measurement using Atomic Force Microscopy

This study demonstrates a novel technique of preparing drug colloid probes to determine the adhesion force between a model drug salbutamol sulphate (SS) and the surfaces of polymer microparticles to be used as carriers for the dispersion of drug particles from dry powder inhaler (DPI) formulations. Model silica probes of approximately 4 lm size, similar to a drug particle used in DPI formulations, were coated with a saturated SS solution with the aid of capillary forces acting between the silica probe and the drug solution. The developed method of ensuring a smooth and uniform layer of SS on the silica probe was validated using X-ray Photoelectron Spectroscopy (XPS) and Scanning Electron Microscopy (SEM). Using the same technique, silica microspheres pre-attached on the AFM cantilever were coated with SS. The adhesion forces between the silica probe and drug coated silica (drug probe) and polymer surfaces (hydrophilic and hydrophobic) were determined. Our experimental results showed that the technique for preparing the drug probe was robust and can be used to determine the adhesion force between hydrophilic/ hydrophobic drug probe and carrier surfaces to gain a better understanding on drug carrier adhesion forces in DPI formulations.

Creating a common approach to safety management through structured training development

The successful management of workplace safety has many benefits for employees, employers and the community. Similar to other areas of job performance, safety performance can be enhanced through appropriate and well-designed training. The foundation of the development of effective training is a thorough training needs analysis (TNA). Currently, the application of psychometrically valid TNA practices for the management of workplace safety is an under-researched topic and limited guidance is available for implementing appropriate strategies. To address this gap in the literature, this chapter will provide an overview of TNA practices, including the purpose and benefits associated with implementing the systematic procedure. A case study will then be presented to illustrate how the TNA process was successfully applied to investigate the training needs of Australasian rail incident investigators to achieve an industry-approved national training package. Recommendations will be made to assist practitioners with implementing TNA practices with the goal of enhancing workplace safety management through targeted workforce development.

Size dependent photodegradation of CdS particles deposited onto TiO2 mesoporous films by SILAR method

The properties of CdS nanoparticles incorporated onto mesoporous TiO2 films by a successive ionic layer adsorption and reaction (SILAR) method were investigated by Raman spectroscopy, UV-visible spectroscopy, transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). High resolution TEM indicated that the synthesized CdS particles were hexagonal phase and the particle sizes were less than 5 nm when SILAR cycles were fewer than 9. Quantum size effect was found with the CdS sensitized TiO2 films prepared with up to 9 SILAR cycles. The band gap of CdS nanoparticles decreased from 2.65 eV to 2.37 eV with the increase of the SILAR cycles from 1 to 11. The investigation of the stability of the CdS/TiO2 films in air under illumination (440.6 µW/cm2) showed that the photodegradation rate was up to 85% per day for the sample prepared with 3 SILAR cycles. XPS analysis indicated that the photodegradation was due to the oxidation of CdS, leading to the transformation from sulphide to sulphate (CdSO4). Furthermore, the degradation rate was strongly dependent upon the particle size of CdS. Smaller particles showed faster degradation rate. The size-dependent photo-induced oxidization was rationalized with the variation of size-dependent distribution of surface atoms of CdS particles. Molecular Dynamics (MD) simulation has indicated that the surface sulphide anion of a large CdS particle such as CdS made with 11 cycles (CdS11, particle size = 5.6 nm) accounts for 9.6% of the material whereas this value is increased to 19.2% for (CdS3) based smaller particles (particle size: 2.7 nm). Nevertheless, CdS nanoparticles coated with ZnS material showed a significantly enhanced stability under illumination in air. A nearly 100% protection of CdS from photon induced oxidation with a ZnS coating layer prepared using four SILAR cycles, suggesting the formation of a nearly complete coating layer on the CdS nanoparticles.

High-yeld RNA-extraction method for saliva

BACKGROUND: The use of salivary diagnostics is increasing because of its noninvasiveness, ease of sampling, and the relatively low risk of contracting infectious organisms. Saliva has been used as a biological fluid to identify and validate RNA targets in head and neck cancer patients. The goal of this study was to develop a robust, easy, and cost-effective method for isolating high yields of total RNA from saliva for downstream expression studies. METHODS: Oral whole saliva (200 mu L) was collected from healthy controls (n = 6) and from patients with head and neck cancer (n = 8). The method developed in-house used QIAzol lysis reagent (Qiagen) to extract RNA from saliva (both cell-free supernatants and cell pellets), followed by isopropyl alcohol precipitation, cDNA synthesis, and real-time PCR analyses for the genes encoding beta-actin ("housekeeping" gene) and histatin (a salivary gland-specific gene). RESULTS: The in-house QIAzol lysis reagent produced a high yield of total RNA (0.89 -7.1 mu g) from saliva (cell-free saliva and cell pellet) after DNase treatment. The ratio of the absorbance measured at 260 nm to that at 280 nm ranged from 1.6 to 1.9. The commercial kit produced a 10-fold lower RNA yield. Using our method with the QIAzol lysis reagent, we were also able to isolate RNA from archived saliva samples that had been stored without RNase inhibitors at -80 degrees C for >2 years. CONCLUSIONS: Our in-house QIAzol method is robust, is simple, provides RNA at high yields, and can be implemented to allow saliva transcriptomic studies to be translated into a clinical setting.

A rapid and cost-effective method of producing recombinant proBNP and NT-proBNP variants in Escherichia coli for immunoassay of heart failure

The measurements of plasma natriuretic peptides (NT-proBNP, proBNP and BNP) are used to diagnose heart failure but these are expensive to produce. We describe a rapid, cheap and facile production of proteins for immunoassays of heart failure. DNA encoding N-terminally His-tagged NT-proBNP and proBNP were cloned into the pJexpress404 vector. ProBNP and NT-proBNP peptides were expressed in Escherichia coli, purified and refolded in vitro. The analytical performance of these peptides were comparable with commercial analytes (NT-proBNP EC50 for the recombinant is 2.6 ng/ml and for the commercial material is 5.3 ng/ml) and the EC50 for recombinant and commercial proBNP, are 3.6 and 5.7 ng/ml respectively). Total yield of purified refolded NT-proBNP peptide was 1.75 mg/l and proBNP was 0.088 mg/l. This approach may also be useful in expressing other protein analytes for immunoassay applications. To develop a cost effective protein expression method in E. coli to obtain high yields of NT-proBNP (1.75 mg/l) and proBNP (0.088 mg/l) peptides for immunoassay use.

A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model

In a tag-based recommender system, the multi-dimensional correlation should be modeled effectively for finding quality recommendations. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based on the maximum values of tensor elements. In order to improve the accuracy and scalability, we propose an implementation of the -mode block-striped (matrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.

The establishment of an ISO compliant cancer biobank for Jordan and its neighboring countries through knowledge transfer and training

Research studies aimed at advancing cancer prevention, diagnosis, and treatment depend on a number of key resources, including a ready supply of high-quality annotated biospecimens from diverse ethnic populations that can be used to test new drugs, assess the validity of prognostic biomarkers, and develop tailor-made therapies. In November 2011, KHCCBIO was established at the King Hussein Cancer Center (KHCC) with the support of Seventh Framework Programme (FP7) funding from the European Union (khccbio.khcc.jo). KHCCBIO was developed for the purpose of achieving an ISO accredited cancer biobank through the collection, processing, and preservation of high-quality, clinically annotated biospecimens from consenting cancer patients, making it the first cancer biobank of its kind in Jordan. The establishment of a state-of-the-art, standardized biospecimen repository of matched normal and lung tumor tissue, in addition to blood components such as serum, plasma, and white blood cells, was achieved through the support and experience of its European partners, Trinity College Dublin, Biostor Ireland, and accelopment AG. To date, KHCCBIO along with its partners, have worked closely in establishing an ISO Quality Management System (QMS) under which the biobank will operate. A Quality Policy Manual, Validation, and Training plan have been developed in addition to the development of standard operating procedures (SOPs) for consenting policies on ethical issues, data privacy, confidentiality, and biobanking bylaws. SOPs have also been drafted according to best international practices and implemented for the donation, procurement, processing, testing, preservation, storage, and distribution of tissues and blood samples from lung cancer patients, which will form the basis for the procurement of other cancer types. KHCCBIO will be the first ISO accredited cancer biobank from a diverse ethnic Middle Eastern and North African population. It will provide a unique and valuable resource of high-quality human biospecimens and anonymized clinicopathological data to the cancer research communities world-wide.

The effect of previous hamstring strain injuries on the change in eccentric hamstring strength during preseason training in elite Australian footballers

Background Hamstring strain injuries (HSIs) are the most common injury type in Australian football and the rate of recurrence has been consistently high for a number of years. Long lasting neuromuscular inhibition has been noted in previously injured athletes but it is not known if this influences athletes adaptive response to training. Purpose To determine if elite Australian footballers with a prior unilateral HSI (previously injured group) display lesser improvements in eccentric hamstring strength during pre-season training compared to athletes without a history of HSI (control group). Study design Prospective cohort study. Methods Ninety-nine elite Australian footballers participated (17 with a history of unilateral HSI in the previous 12 month period). Eccentric hamstring strength was assessed at the start and end of pre-season training using an instrumented Nordic hamstring device. Change in eccentric strength across preseason was determine in absolute terms and normalised to start of preseason strength. Start of preseason strength was used as a covariate to control for differences in starting strength. Results The left and right limbs in the control group showed no difference in absolute or relative change (left limb absolute change, 60.7±72.9N; relative change, 1.28±0.34; right limb absolute change, 48.6±83.8N; relative change, 1.24±0.43) . Similarly, the injured and uninjured limbs from the previously injured group showed no difference for either absolute or relative measures of change (injured limb absolute change, 13.1±57.7N; relative change, 1.07±0.18; uninjured limb absolute change, 14.7±54.0N; relative change, 1.07±0.22N). The previously injured group displayed a significantly lesser increase in eccentric hamstring strength across the preseason (absolute change, 13.9±55.0; relative change, 1.07±0.20) compared to the control group (absolute change, 54.6±78.5; relative change, 1.26±0.39) for both absolute and relative measures (p < 0.001), even after controlling for differences in start of pre-season eccentric hamstring strength, which had a significant effect on strength improvement. Conclusion Elite Australian footballers with a unilateral HSI history displayed lesser improvements in eccentric hamstring strength across preseason training. The smaller improvements were not restricted to the previously injured limb as the contralateral limb also displayed similarly small improvements in eccentric strength. Whether this is the cause of or the result of injury remains to be seen, but it has the potential to contribute to the risk of hamstring strain re-injury.

An MRF based abnormal event detection approach using motion and appearance features

Abnormal event detection has attracted a lot of attention in the computer vision research community during recent years due to the increased focus on automated surveillance systems to improve security in public places. Due to the scarcity of training data and the definition of an abnormality being dependent on context, abnormal event detection is generally formulated as a data-driven approach where activities are modeled in an unsupervised fashion during the training phase. In this work, we use a Gaussian mixture model (GMM) to cluster the activities during the training phase, and propose a Gaussian mixture model based Markov random field (GMM-MRF) to estimate the likelihood scores of new videos in the testing phase. Further-more, we propose two new features: optical acceleration, and the histogram of optical flow gradients; to detect the presence of any abnormal objects and speed violations in the scene. We show that our proposed method outperforms other state of the art abnormal event detection algorithms on publicly available UCSD dataset.

Stochastic ranking method for thermostatically controllable appliances to provide regulation services

Demand response can be used for providing regulation services in the electricity markets. The retailers can bid in a day-ahead market and respond to real-time regulation signal by load control. This paper proposes a new stochastic ranking method to provide regulation services via demand response. A pool of thermostatically controllable appliances (TCAs) such as air conditioners and water heaters are adjusted using direct load control method. The selection of appliances is based on a probabilistic ranking technique utilizing attributes such as temperature variation and statuses of TCAs. These attributes are stochastically forecasted for the next time step using day-ahead information. System performance is analyzed with a sample regulation signal. Network capability to provide regulation services under various seasons is analyzed. The effect of network size on the regulation services is also investigated.

Fuzzy logic to evaluate driving maneuvers: An integrated approach to improve training

Driver training is one of the interventions aimed at mitigating the number of crashes that involve novice drivers. Our failure to understand what is really important for learners, in terms of risky driving, is one of the many drawbacks restraining us to build better training programs. Currently, there is a need to develop and evaluate Advanced Driving Assistance Systems that could comprehensively assess driving competencies. The aim of this paper is to present a novel Intelligent Driver Training System (IDTS) that analyses crash risks for a given driving situation, providing avenues for improvement and personalisation of driver training programs. The analysis takes into account numerous variables acquired synchronously from the Driver, the Vehicle and the Environment (DVE). The system then segments out the manoeuvres within a drive. This paper further presents the usage of fuzzy set theory to develop the safety inference rules for each manoeuvre executed during the drive. This paper presents a framework and its associated prototype that can be used to comprehensively view and assess complex driving manoeuvres and then provide a comprehensive analysis of the drive used to give feedback to novice drivers.