Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Systematic Evaluation of Extraction Methods for Multiplatform-Based Metabotyping: Application to the Fasciola hepatica Metabolome

Combining data from multiple analytical platforms is essential for comprehensive study of the molecular phenotype (metabotype) of a given biological sample. The metabolite profiles generated are intrinsically dependent on the analytical platforms, each requiring optimization of instrumental parameters, separation conditions, and sample extraction to deliver maximal biological information. An in-depth evaluation of extraction protocols for characterizing the metabolome of the hepatobiliary fluke Fasciola hepatica, using ultra performance liquid chromatography and capillary electrophoresis coupled with mass spectroscopy is presented. The spectrometric methods were characterized by performance, and metrics of merit were established, including precision, mass accuracy, selectivity, sensitivity, and platform stability. Although a core group of molecules was common to all methods, each platform contributed a unique set, whereby 142 metabolites out of 14,724 features were identified. A mixture design revealed that the chloroform:methanol:water proportion of 15:59:26 was globally the best composition for metabolite extraction across UPLC-MS and CE-MS platforms accommodating different columns and ionization modes. Despite the general assumption of the necessity of platform-adapted protocols for achieving effective metabotype characterization, we show that an appropriately designed single extraction procedure is able to fit the requirements of all technologies. This may constitute a paradigm shift in developing efficient protocols for high-throughput metabolite profiling with more-general analytical applicability.

Properties of nanoparticles prepared from NdFeB-based compound for magnetic hyperthermia application

Nanoparticles were prepared from a NdFeB-based alloy using the hydrogen decrepitation process together with high-energy ball milling and tested as heating agent for magnetic hyperthermia. In the milling time range evaluated (up to 10 h), the magnetic moment per mass at H = 1.59 MA m(-1) is superior than 70 A m(2) kg(-1); however, the intrinsic coercivity might be inferior than 20 kA m(-1). The material presents both ferromagnetic and superparamagnetic particles constituted by a mixture of phases due to the incomplete disproportionation reaction of Nd2Fe14BHx during milling. Solutions prepared with deionized water and magnetic particles exposed to an AC magnetic field (H-max similar to 3.7 kA m(-1) and f = 228 kHz) exhibited 26 K <= Delta T-max <= 44 K with a maximum estimated specific absorption rate (SAR) of 225 W kg(-1). For the pure magnetic material milled for the longest period of time (10 h), the SAR was estimated as similar to 2500 W kg(-1). In vitro tests indicated that the powders have acceptable cytotoxicity over a wide range of concentration (0.1-100 mu g ml(-1)) due to the coating applied during milling.

Agar-Based Gel Electrolyte for Electrochromic Device Application

Gel Polymer Electrolytes (GPE) based on agar and containing LiClO4 have been prepared, characterized and applied to electrochromic devices. The ionic conductivity revealed the best result of 6.5 x 10(-5) S/cm for the sample with 17 wt.% of LiClO4, which increased to 5.4 x 10(-4) S/cm at 72 degrees C. TheGPE have been used in electrochromic devices (ECD) with K-glass/WO3/GPE/CeO2-TiO2/K-glass configuration. The ECD changed transmittance values up to 30% between the colored and transparent states. The charge density measurements revealed an increase of 5.5 to 7.5 mC/cm(2) from the first to 500th cycles and then a decrease to 4.4 mC/cm(2) during the next 4500 cycles. Coloration efficiency (eta) of 25 cm(2)/C was obtained.

Detection of gait perturbations based on proprioceptive information. Application to Limit Cycle Walkers

Walking on irregular surfaces and in the presence of unexpected events is a challenging problem for bipedal machines. Up to date, their ability to cope with gait disturbances is far less successful than humans': Neither trajectory controlled robots, nor dynamic walking machines (Limit CycleWalkers) are able to handle them satisfactorily. On the contrary, humans reject gait perturbations naturally and efficiently relying on their sensory organs that, if needed, elicit a recovery action. A similar approach may be envisioned for bipedal robots and exoskeletons: An algorithm continuously observes the state of the walker and, if an unexpected event happens, triggers an adequate reaction. This paper presents a monitoring algorithm that provides immediate detection of any type of perturbation based solely on a phase representation of the normal walking of the robot. The proposed method was evaluated in a Limit Cycle Walker prototype that suffered push and trip perturbations at different moments of the gait cycle, providing 100% successful detections for the current experimental apparatus and adequately tuned parameters, with no false positives when the robot is walking unperturbed.

Synthesis and Solar Cell Application of New Alternating Donor-Acceptor Copolymers Based on Variable Units of Fluorene, Thiophene, and Phenylene

A new series of donor acceptor copolymers were synthesized via the Witting route and applied as an active layer in organic thin-films solar cells. These copolymers are composed of fluorene thiophene and phenylene thiophene units. The ratio between those was systematically varied, and copolymers containing 0%, 50%, and 75% of phenylene thiophene were characterized and evaluated when used in photovoltaic devices. The copolymers' composition, photophysical, electrical, and morphological properties are addressed and correlated with device performance. The 50% copolymer ratio was found to be the best copolymer of the series, yielding a power conversion efficiency (PCE) under air mass (AM) 1.5 conditions of 2.4% in the bilayer heterojunction with the C-60 molecule. Aiming at flexible electronics applications, solutions based on the heterojunction of this copolymer with PCBM (6,6-phenyl-C-61-butyric acid methyl ester) were also successfully deposited using an inkjet printing method and used as an active layer in solar cells.

Different approaches for dealing with rare variants in family-based genetic studies: application of a Genetic Analysis Workshop 17 problem

Rare variants are becoming the new candidates in the search for genetic variants that predispose individuals to a phenotype of interest. Their low prevalence in a population requires the development of dedicated detection and analytical methods. A family-based approach could greatly enhance their detection and interpretation because rare variants are nearly family specific. In this report, we test several distinct approaches for analyzing the information provided by rare and common variants and how they can be effectively used to pinpoint putative candidate genes for follow-up studies. The analyses were performed on the mini-exome data set provided by Genetic Analysis Workshop 17. Eight approaches were tested, four using the trait’s heritability estimates and four using QTDT models. These methods had their sensitivity, specificity, and positive and negative predictive values compared in light of the simulation parameters. Our results highlight important limitations of current methods to deal with rare and common variants, all methods presented a reduced specificity and, consequently, prone to false positive associations. Methods analyzing common variants information showed an enhanced sensibility when compared to rare variants methods. Furthermore, our limited knowledge of the use of biological databases for gene annotations, possibly for use as covariates in regression models, imposes a barrier to further research.