Beyond managerial talent: ‘key group’ identification and differential compensation practices in multinational companies

With the maturation of strategic human resource management scholarship, there appears to be a greater call to move from monolithic workforce management to a more strategic and differentiated emphasis on employees with the greatest capacity to enhance competitive advantage. There has been little consideration in the literature as to whether organizations formally identify key groups of employees based on their impact on organizational learning and core competences. Using survey evidence from 260 multinational companies (MNCs), this paper explores the extent to which key groups of employees are formally recognized and whether they are subject to differential compensation practices. The results demonstrate that just in excess of half of these MNCs identify a key group. There was considerable differentiation in the compensation practices between these key groups, managers and the largest occupational group in the workforce. The results give rise to questions worthy of future investigation, namely whether the differentiated approaches used lead to improved performance outcomes.

Model Selection in SVMs Using Differential Evolution

To improve the performance of classification using Support Vector Machines (SVMs) while reducing the model selection time, this paper introduces Differential Evolution, a heuristic method for model selection in two-class SVMs with a RBF kernel. The model selection method and related tuning algorithm are both presented. Experimental results from application to a selection of benchmark datasets for SVMs show that this method can produce an optimized classification in less time and with higher accuracy than a classical grid search. Comparison with a Particle Swarm Optimization (PSO) based alternative is also included.

Identification of a suitable sterilisation method for collagen derived from a marine Demosponge

Collagen is widely used as a biomedical material, and its importance is likely to grow as research and understanding progresses in this field. As a biomedical material, ensuring the sterility of collagen before use as, or incorporation into, a medical device is paramount. However, common sterilisation techniques can induce changes in the physical structure and protein chemistry of collagen, potentially affecting the performance. In this preliminary study, the influence of autoclaving, gamma irradiation and ethylene oxide gas sterilisation on the denaturation temperature and helical content of the collagen was evaluated using differential scanning calorimetry and Fourier transform infrared spectroscopy. Early results indicate that all sterilisation techniques affect collagen properties but suggest that the least damaging of the techniques investigated was y irradiation.

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping. © 2013 McArt et al.

Intelligent potentiostat for identification of heavy metals in situ

This article presents a low-cost portable electrochemical instrument capable of on-site identification of heavy metals. The instrument acquires metal-specific voltage and current signals by the application of differential pulse anodic stripping voltammetry. This technique enhances the analytical current and rejects the background current, resulting in a higher signal-to-noise ratio for a better detection limit. The identification of heavy metals is based on an intelligent machine-based method using a multilayer perceptron neural network consisting of three layers of neurons. The neural network is implemented using a 16 bit microcontroller. The system is developed for use in the field in order to avoid expensive and time-consuming procedures and can be used in a variety of situations to help environmental assessment and control. 

A differential protein solubility approach for the depletion of highly abundant proteins in plasma using ammonium sulfate

Depletion of highly abundant proteins is an approved step in blood plasma analysis by mass spectrometry (MS). In this study, we explored a precipitation and differential protein solubility approach as a fractionation strategy for abundant protein removal from plasma. Total proteins from plasma were precipitated with 90% saturated ammonium sulfate, followed by differential solubilization in 55% and 35% saturated ammonium sulfate solutions. Using a four hour liquid chromatography (LC) gradient and an LTQ-Orbitrap XL mass spectrometer, a total of 167 and 224 proteins were identified from the 55% and 35% ammonium sulfate fractions, whereas 235 proteins were found in the remaining protein fractions with at least two unique peptides. SDS-PAGE and exclusive total spectrum counts from LC-MS/MS analyses clearly showed that majority of the abundant plasma proteins were solubilized in 55% and 35% ammonium sulfate solutions, indicating that the remaining protein fraction is of potential interest for identification of less abundant plasma proteins. Serum albumin, serotransferrin, alpha-1-antitrypsin and transthyretin were the abundant proteins that were highly enriched in 55% ammonium sulfate fractions. Immunoglobulins, complement system proteins, and apolipoproteins were among other abundant plasma proteins that were enriched in 35% ammonium sulfate fractions. In the remaining protein fractions a total of 40 unique proteins were identified of which, 32 proteins were identified with at least 10 exclusive spectrum counts. According to PeptideAtlas, 9 of these 32 proteins were estimated to be present at low μg ml(-1) (0.12-1.9 μg ml(-1)) concentrations in the plasma, and 17 at low ng ml(-1) (0.1-55 ng ml(-1)) range.

Irish Credit Unions: Differential Regulation based on Business Model Complexity

This study examines the business model complexity of Irish credit unions using a latent class approach to measure structural performance over the period 2002 to 2013. The latent class approach allows the endogenous identification of a multi-class framework for business models based on credit union specific characteristics. The analysis finds a three class system to be appropriate with the multi-class model dependent on three financial viability characteristics. This finding is consistent with the deliberations of the Irish Commission on Credit Unions (2012) which identified complexity and diversity in the business models of Irish credit unions and recommended that such complexity and diversity could not be accommodated within a one size fits all regulatory framework. The analysis also highlights that two of the classes are subject to diseconomies of scale. This may suggest credit unions would benefit from a reduction in scale or perhaps that there is an imbalance in the present change process. Finally, relative performance differences are identified for each class in terms of technical efficiency. This suggests that there is an opportunity for credit unions to improve their performance by using within-class best practice or alternatively by switching to another class.