Understanding New Product Project Performance

Purpose – The purpose of this paper is to summarize the accumulated body of knowledge on the performance of new product projects and provide directions for further research. Design/methodology/approach – Using a refined classification of antecedents of new product project performance the research results are meta-analyzed in the literature in order to identify the strength and stability of predictor-performance relationships. Findings – The results reveal that 22 variables have a significant relationship with new product project performance, of which only 12 variables have a sizable relationship. In order of importance these factors are the degree of organizational interaction, R&D and marketing interface, general product development proficiency, product advantage, financial/business analysis, technical proficiency, management skill, marketing proficiency, market orientation, technology synergy, project manager competency and launch activities. Of the 34 variables 16 predictors show potential for moderator effects. Research limitations/implications – The validity of the results is constrained by publication bias and heterogeneity of performance measures, and directions for the presentation of data in future empirical publications are provided. Practical implications – This study helps new product project managers in understanding and managing the performance of new product development projects. Originality/value – This paper provides unique insights into the importance of predictors of new product performance at the project level. Furthermore, it identifies which predictor-performance relations are contingent on other factors.

A concept analysis of renal supportive care: the changing world of nephrology

Title. A concept analysis of renal supportive care: the changing world of nephrology

Aim. This paper is a report of a concept analysis of renal supportive care.

Background. Approximately 1.5 million people worldwide are kept alive by renal dialysis. As services are required to support patients who decide not to start or to withdraw from dialysis, the term renal supportive care is emerging. Being similar to the terms palliative care, end-of-life care, terminal care and conservative management, there is a need for conceptual clarity.

Method. Rodgers' evolutionary method was used as the organizing framework for this concept analysis. Data were collected from a review of CINAHL, Medline, PsycINFO, British Nursing Index, International Bibliography of the Social Sciences and ASSIA (1806-2006) using, 'renal' and 'supportive care' as keywords. All articles with an abstract were considered. The World Wide Web was also searched in English utilizing the phrase 'renal supportive care'.

Results. Five attributes of renal supportive care were identified: available from diagnosis to death with an emphasis on honesty regarding prognosis and impact of disease; interdisciplinary approach to care; restorative care; family and carer support and effective, lucid communication to ensure informed choice and clear lines of decision-making.

Conclusion. Renal supportive care is a dynamic and emerging concept relevant, but not limited to, the end phase of life. It suggests a central philosophy underpinning renal service development that allows patients, carers and the multidisciplinary team time to work together to realize complex goals. It has relevance for the renal community and is likely to be integrated increasingly into everyday nephrology practice.

Improving Patient Safety Through Formal and Informal Learning Networks

This paper explores a novel perspective on patient safety improvements, which draws on
contemporary social network and learning theories. A case study was conducted at a Portuguese
acute university hospital. Data collection followed a staged approach, whereby 46 interviews
were conducted involving 49 respondents from a broad array of departments and professional
backgrounds. This case study highlights the importance of two major interlinked factors in
contributing to patient safety improvements. The first of these is the crucial role of formal and
informal, internal and external social networks. The second is the importance and the possible
advantage of combining formal and informal learning. The analysis suggests that initiatives
rooted in formal learning approaches alone do not necessarily lead to the creation of long-term
grounded internal safety networks, and that patient safety improvements can crucially depend on
bottom-up initiatives of communities of practice and informal learning. Traditional research on
patient safety places a strong emphasis on top-down and managerialist approaches and is often
based on the assumption that „safety? learning is primarily formal and context-independent. This
paper suggests that bottom-up initiatives and a combination of formal and informal learning can
make a major contribute to patient safety improvements.

Combined genomic and phenotype screening reveals secretory factor SPINK1 as an invasion and survival factor associated with patient prognosis in breast cancer

Secretory factors that drive cancer progression are attractive immunotherapeutic targets. We used a whole-genome data-mining approach on multiple cohorts of breast tumours annotated for clinical outcomes to discover such factors. We identified Serine protease inhibitor Kazal-type 1 (SPINK1) to be associated with poor survival in estrogen receptor-positive (ER+) cases. Immunohistochemistry showed that SPINK1 was absent in normal breast, present in early and advanced tumours, and its expression correlated with poor survival in ER+ tumours. In ER- cases, the prognostic effect did not reach statistical significance. Forced expression and/or exposure to recombinant SPINK1 induced invasiveness without affecting cell proliferation. However, down-regulation of SPINK1 resulted in cell death. Further, SPINK1 overexpressing cells were resistant to drug-induced apoptosis due to reduced caspase-3 levels and high expression of Bcl2 and phospho-Bcl2 proteins. Intriguingly, these anti-apoptotic effects of SPINK1 were abrogated by mutations of its protease inhibition domain. Thus, SPINK1 affects multiple aggressive properties in breast cancer: survival, invasiveness and chemoresistance. Because SPINK1 effects are abrogated by neutralizing antibodies, we suggest that SPINK1 is a viable potential therapeutic target in breast cancer.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Choosing and using diversity indices: Insights for ecological applications from the German Biodiversity Exploratories

Biodiversity, a multidimensional property of natural systems, is difficult to quantify partly because of the multitude of indices proposed for this purpose. Indices aim to describe general properties of communities that allow us to compare different regions, taxa, and trophic levels. Therefore, they are of fundamental importance for environmental monitoring and conservation, although there is no consensus about which indices are more appropriate and informative. We tested several common diversity indices in a range of simple to complex statistical analyses in order to determine whether some were better suited for certain analyses than others. We used data collected around the focal plant Plantago lanceolata on 60 temperate grassland plots embedded in an agricultural landscape to explore relationships between the common diversity indices of species richness (S), Shannon's diversity (H'), Simpson's diversity (D1), Simpson's dominance (D2), Simpson's evenness (E), and Berger–Parker dominance (BP). We calculated each of these indices for herbaceous plants, arbuscular mycorrhizal fungi, aboveground arthropods, belowground insect larvae, and P. lanceolata molecular and chemical diversity. Including these trait-based measures of diversity allowed us to test whether or not they behaved similarly to the better studied species diversity. We used path analysis to determine whether compound indices detected more relationships between diversities of different organisms and traits than more basic indices. In the path models, more paths were significant when using H', even though all models except that with E were equally reliable. This demonstrates that while common diversity indices may appear interchangeable in simple analyses, when considering complex interactions, the choice of index can profoundly alter the interpretation of results. Data mining in order to identify the index producing the most significant results should be avoided, but simultaneously considering analyses using multiple indices can provide greater insight into the interactions in a system.

Digital pathology and image analysis in tissue biomarker research

Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research for biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image analysis, with a particular focus on research and biomarker discovery. A variety of image analysis applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence analysis of tissue biomarkers. Digital pathology and image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology, tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high quality tissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital pathology laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological, clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as – weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases – i) discovering homogeneous regions, and ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

Fast Rule Mining over Multi-dimensional Windows

Association rule mining is an indispensable tool for discovering
insights from large databases and data warehouses.
The data in a warehouse being multi-dimensional, it is often
useful to mine rules over subsets of data defined by selections
over the dimensions. Such interactive rule mining
over multi-dimensional query windows is difficult since rule
mining is computationally expensive. Current methods using
pre-computation of frequent itemsets require counting
of some itemsets by revisiting the transaction database at
query time, which is very expensive. We develop a method
(RMW) that identifies the minimal set of itemsets to compute
and store for each cell, so that rule mining over any
query window may be performed without going back to the
transaction database. We give formal proofs that the set of
itemsets chosen by RMW is sufficient to answer any query
and also prove that it is the optimal set to be computed
for 1 dimensional queries. We demonstrate through an extensive
empirical evaluation that RMW achieves extremely
fast query response time compared to existing methods, with
only moderate overhead in pre-computation and storage