Comparing and complementing methods : traditional market research vs. deep customer insights

Quantitative market data has traditionally been used throughout marketing and business as a tool to inform and direct design decisions. However, in our changing economic climate, businesses need to innovate and create products their customers will love. Deep customer insight methods move beyond just questioning customers and aims to provoke true emotional responses in order to reveal new opportunities that go beyond functional product requirements. This paper explores traditional market research methods and compares them to methods used to gain deep customer insights. This study reports on a collaborative research project with seven small to medium enterprises and four multi-national organisations. Firms were introduced to a design led innovation approach, and were taught the different methods to gain deep customer insights. Interviews were conducted to understand the experience and outcomes of pre-existing research methods and deep customer insight approaches. Findings concluded that deep customer insights were unlikely to be revealed through traditional market research techniques. The theoretical outcome of this study is a complementary methods matrix, providing guidance on appropriate research methods in accordance to a project’s timeline.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Purpose: Flat-detector, cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. Methods: The rich sources of prior information in IGRT are incorporated into a hidden Markov random field (MRF) model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk (OAR). The voxel labels are estimated using the iterated conditional modes (ICM) algorithm. Results: The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom (CIRS, Inc. model 062). The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. Conclusions: By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Impact of extracellular matrix derived from osteoarthritis subchondral bone osteoblasts on osteocytes : role of integrinβ1 and focal adhesion kinase signaling cues

INTRODUCTION: Our recent study indicated that subchondral bone pathogenesis in osteoarthritis (OA) is associated with osteocyte morphology and phenotypic abnormalities. However, the mechanism underlying this abnormality needs to be identified. In this study we investigated the effect of extracellular matrix (ECM) produced from normal and OA bone on osteocytic cells function. METHODS: De-cellularized matrices, resembling the bone provisional ECM secreted from primary human subchondral bone osteoblasts (SBOs) of normal and OA patients were used as a model to study the effect on osteocytic cells. Osteocytic cells (MLOY4 osteocyte cell line) cultured on normal and OA derived ECMs were analyzed by confocal microscopy, scanning electron microscopy (SEM), cell attachment assays, zymography, apoptosis assays, qRT-PCR and western blotting. The role of integrinβ1 and focal adhesion kinase (FAK) signaling pathways during these interactions were monitored using appropriate blocking antibodies. RESULTS: The ECM produced by OA SBOs contained less mineral content, showed altered organization of matrix proteins and matrix structure compared with the matrices produced by normal SBOs. Culture of osteocytic cells on these defective OA ECM resulted in a decrease of integrinβ1 expression and the de-activation of FAK cell signaling pathway, which subsequently affected the initial osteocytic cell's attachment and functions including morphological abnormalities of cytoskeletal structures, focal adhesions, increased apoptosis, altered osteocyte specific gene expression and increased Matrix metalloproteinases (MMP-2) and -9 expression. CONCLUSION: This study provides new insights in understanding how altered OA bone matrix can lead to the abnormal osteocyte phenotypic changes, which is typical in OA pathogenesis.

Principle components analysis with covariance matrix on regional differences for mortality and percentages of cancers in Shandong province [山东省恶性肿瘤死亡率和构成比地区差异协方差矩阵主成分分析]

Objective To explore the characteristics of regional distribution of cancer deaths in Shandong Province with the principle components analysis. Methods The principle components analysis with co-variance matrix for age-adjusted mortality rates and percentages of 20 types of cancer in 22 counties （cities） were carried out using SAS Software. Results Over 90% of the total information could be reflected by the top 3 principle components and the first principle component alone represented more than half of the overall regional variances. The first component mainly reflected the area differences of esophageal cancer. The second component mainly reflected the area differences of lung cancer， stomach cancer and liver cancer. The value of the first principal component scores showed a clear trend that the west areas possessed higher values and the east the lower values. Based on the top two components，the 22 counties （cities） could be divided into several geographical clusters. Conclusion The overall difference of regional distribution of cancers in Shandong is dominated by several major cancers including esophageal cancer， lung cancer， stomach cancer and liver cancer. Among them，esophageal cancer makes the largest contribution. If the range of counties （cities） analyzed could be further widened， the characteristics of regional distribution of cancer mortality would be better examined. Abstract in Chinese 目的 利用主成分分析探讨山东省恶性肿瘤死亡的地区分布特征. 方法 利用SAS软件对山东省22个县市区2004～2006午的20种恶性肿瘤标化死亡率和构成比分别进行协方差矩阵主成分分析. 结果 前3个主成分就反映了总体差异90%以上的信息,其中仅第1主成分就提供了总体差异一半以上的信息.第1主成分主要反映了食管癌的地区差异,第2主成分主要反映肺癌的地区差异,兼顾胃癌和肝癌.各地区第1主成分得分呈现西高东低的趋势,根据第1和第2主成分可以将调查地区分为若干类别,表现为明显的地理聚集性. 结论 山东省各地区恶性肿瘤死亡的总体差异主要取决于少数高发肿瘤,包括食管癌、肺癌、胃癌、肝癌等,其中以食管癌地位最为突出.如能进一步扩大分析范围,可更好地查明恶性肿瘤死亡的地区特征.

A novel method for finding similarities between unordered trees using matrix data model

Trees are capable of portraying the semi-structured data which is common in web domain. Finding similarities between trees is mandatory for several applications that deal with semi-structured data. Existing similarity methods examine a pair of trees by comparing through nodes and paths of two trees, and find the similarity between them. However, these methods provide unfavorable results for unordered tree data and result in yielding NP-hard or MAX-SNP hard complexity. In this paper, we present a novel method that encodes a tree with an optimal traversing approach first, and then, utilizes it to model the tree with its equivalent matrix representation for finding similarity between unordered trees efficiently. Empirical analysis shows that the proposed method is able to achieve high accuracy even on the large data sets.

Phase III study of matrix metalloproteinase inhibitor prinomastat in non-small-cell lung cancer

Purpose: Matrix metalloproteinases (MMPs) degrade extracellular proteins and facilitate tumor growth, invasion, metastasis, and angiogenesis. This trial was undertaken to determine the effect of prinomastat, an inhibitor of selected MMPs, on the survival of patients with advanced non-small-cell lung cancer (NSCLC), when given in combination with gemcitabine-cisplatin chemotherapy. Patients and Methods: Chemotherapy-naive patients were randomly assigned to receive prinomastat 15 mg or placebo twice daily orally continuously, in combination with gemcitabine 1,250 mg/m2 days 1 and 8 plus cisplatin 75 mg/m2 day 1, every 21 days for up to six cycles. The planned sample size was 420 patients. Results: Study results at an interim analysis and lack of efficacy in another phase III trial prompted early closure of this study. There were 362 patients randomized (181 on prinomastat and 181 on placebo). One hundred thirty-four patients had stage IIIB disease with T4 primary tumor, 193 had stage IV disease, and 34 had recurrent disease (one enrolled patient was ineligible with stage IIIA disease). Overall response rates for the two treatment arms were similar (27% for prinomastat v 26% for placebo; P = .81). There was no difference in overall survival or time to progression; for prinomastat versus placebo patients, the median overall survival times were 11.5 versus 10.8 months (P = .82), 1-year survival rates were 43% v 38% (P = .45), and progression-free survival times were 6.1 v 5.5 months (P = .11), respectively. The toxicities of prinomastat were arthralgia, stiffness, and joint swelling. Treatment interruption was required in 38% of prinomastat patients and 12% of placebo patients. Conclusion: Prinomastat does not improve the outcome of chemotherapy in advanced NSCLC. © 2005 by American Society of Clinical Oncology.

Vectors and methods for hairpin RNA and artificial microRNA-mediated gene silencing in plants

In plant cells, DICER-LIKE4 processes perfectly double-stranded RNA (dsRNA) into short interfering (si) RNAs, and DICER-LIKE1 generates micro (mi) RNAs from primary miRNA transcripts (pri-miRNA) that form fold-back structures of imperfectly dsRNA. Both si and miRNAs direct the endogenous endonuclease, ARGONAUTE1 to cleave complementary target single-stranded RNAs and either small RNA (sRNA)-directed pathway can be harnessed to silence genes in plants. A routine way of inducing and directing RNA silencing by siRNAs is to express self-complementary single-stranded hairpin RNA (hpRNA), in which the duplexed region has the same sequence as part of the target gene's mRNA. Artificial miRNA (amiRNA)-mediated silencing uses an endogenous pri-miRNA, in which the original miRNA/miRNA* sequence has been replaced with a sequence complementary to the new target gene. In this chapter, we describe the plasmid vector systems routinely used by our research group for the generation of either hpRNA-derived siRNAs or amiRNAs.

Improving short utterance i-vector speaker verification using utterance variance modelling and compensation techniques

This paper proposes techniques to improve the performance of i-vector based speaker verification systems when only short utterances are available. Short-length utterance i-vectors vary with speaker, session variations, and the phonetic content of the utterance. Well established methods such as linear discriminant analysis (LDA), source-normalized LDA (SN-LDA) and within-class covariance normalisation (WCCN) exist for compensating the session variation but we have identified the variability introduced by phonetic content due to utterance variation as an additional source of degradation when short-duration utterances are used. To compensate for utterance variations in short i-vector speaker verification systems using cosine similarity scoring (CSS), we have introduced a short utterance variance normalization (SUVN) technique and a short utterance variance (SUV) modelling approach at the i-vector feature level. A combination of SUVN with LDA and SN-LDA is proposed to compensate the session and utterance variations and is shown to provide improvement in performance over the traditional approach of using LDA and/or SN-LDA followed by WCCN. An alternative approach is also introduced using probabilistic linear discriminant analysis (PLDA) approach to directly model the SUV. The combination of SUVN, LDA and SN-LDA followed by SUV PLDA modelling provides an improvement over the baseline PLDA approach. We also show that for this combination of techniques, the utterance variation information needs to be artificially added to full-length i-vectors for PLDA modelling.

2010–2011 Queensland floods : using Haddon's Matrix to define and categorise public safety strategies

Objective The 2010–2011 Queensland floods resulted in the most deaths from a single flood event in Australia since 1916. This article analyses the information on these deaths for comparison with those from previous floods in modern Australia in an attempt to identify factors that have contributed to those deaths. Haddon's Matrix, originally designed for prevention of road trauma, offers a framework for understanding the interplay between contributing factors and helps facilitate a clearer understanding of the varied strategies required to ensure people's safety for particular flood types. Methods Public reports and flood relevant literature were searched using key words ‘flood’, ‘fatality’, ‘mortality’, ‘death’, ‘injury’ and ‘victim’ through Google Scholar, PubMed, ProQuest and EBSCO. Data relating to reported deaths during the 2010–2011 Queensland floods, and relevant data of previous Australian flood fatality (1997–2009) were collected from these available sources. These sources were also used to identify contributing factors. Results There were 33 deaths directly attributed to the event, of which 54.5% were swept away in a flash flood on 10 January 2011. A further 15.1% of fatalities were caused by inappropriate behaviours. This is different to floods in modern Australia where over 90% of deaths are related to the choices made by individuals. There is no single reason why people drown in floods, but rather a complex interplay of factors. Conclusions The present study and its integration of research findings and conceptual frameworks might assist governments and communities to develop policies and strategies to prevent flood injury and fatalities.

Learning detectors quickly with stationary statistics

Computer vision is increasingly becoming interested in the rapid estimation of object detectors. The canonical strategy of using Hard Negative Mining to train a Support Vector Machine is slow, since the large negative set must be traversed at least once per detector. Recent work has demonstrated that, with an assumption of signal stationarity, Linear Discriminant Analysis is able to learn comparable detectors without ever revisiting the negative set. Even with this insight, the time to learn a detector can still be on the order of minutes. Correlation filters, on the other hand, can produce a detector in under a second. However, this involves the unnatural assumption that the statistics are periodic, and requires the negative set to be re-sampled per detector size. These two methods differ chie y in the structure which they impose on the co- variance matrix of all examples. This paper is a comparative study which develops techniques (i) to assume periodic statistics without needing to revisit the negative set and (ii) to accelerate the estimation of detectors with aperiodic statistics. It is experimentally verified that periodicity is detrimental.

Efficiency comparison of Markov Chain Monte Carlo simulation with subset simulation (MCMC/ss) to standard Monte Carlo Simulation (sMC) for extreme event scenarios

Standard Monte Carlo (sMC) simulation models have been widely used in AEC industry research to address system uncertainties. Although the benefits of probabilistic simulation analyses over deterministic methods are well documented, the sMC simulation technique is quite sensitive to the probability distributions of the input variables. This phenomenon becomes highly pronounced when the region of interest within the joint probability distribution (a function of the input variables) is small. In such cases, the standard Monte Carlo approach is often impractical from a computational standpoint. In this paper, a comparative analysis of standard Monte Carlo simulation to Markov Chain Monte Carlo with subset simulation (MCMC/ss) is presented. The MCMC/ss technique constitutes a more complex simulation method (relative to sMC), wherein a structured sampling algorithm is employed in place of completely randomized sampling. Consequently, gains in computational efficiency can be made. The two simulation methods are compared via theoretical case studies.

Random projections on manifolds of symmetric positive definite matrices for image classification

Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information. The RKHS approach retains more of the manifold structure, but may require non-trivial effort to kernelise Euclidean-based learning algorithms. In contrast to the above approaches, in this paper we offer a novel solution that allows SPD matrices to be used with unmodified Euclidean-based learning algorithms, with the true manifold shape well-preserved. Specifically, we propose to project SPD matrices using a set of random projection hyperplanes over RKHS into a random projection space, which leads to representing each matrix as a vector of projection coefficients. Experiments on face recognition, person re-identification and texture classification show that the proposed approach outperforms several recent methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian Locality Preserving Projection and Relational Divergence Classification.

Multi-shot person re-identification via relational stein divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.

Pro-matrix metalloproteinase-2 transfection increases orthotopic primary growth and experimental metastasis of MDA-MB-231 human breast cancer cells in nude mice

The ability to activate pro-matrix metalloproteinase (pro-MMP)-2 via membrane type-MMP is a hallmark of human breast cancer cell lines that show increased invasiveness, suggesting that MMP-2 contributes to human breast cancer progression. To investigate this, we have stably transfected pro-MMP-2 into the human breast cancer cell line MDA-MB-231, which lacks MMP-2 expression but does express its cell surface activator, membrane type 1-MMP. Multiple clones were derived and shown to produce pro-MMP-2 and to activate it in response to concanavalin A. In vitro analysis showed that the pro-MMP-2-transfected clones exhibited an increased invasive potential in Boyden chamber and Matrigel outgrowth assays, compared with the parental cells or those transfected with vector only. When inoculated into the mammary fat pad of nude mice, each of the MMP-2-tranfected clones grew faster than each of the vector controls tested. After intracardiac inoculation into nude mice, pro-MMP-2-transfected clones showed a significant increase in the incidence of metastasis to brain, liver, bone, and kidney compared with the vector control clones but not lung. Increased tumor burden was seen in the primary site and in lung metastases, and a trend toward increased burden was seen in bone, however, no change was seen in brain, liver, or kidney. This data supports a role for MMP-2 in breast cancer progression, both in the growth of primary tumors and in their spread to distant organs. MMP-2 may be a useful target for breast cancer therapy when refinement of MMP inhibitors provides for MMP-specific agents.

Myogel, a novel, basement membrane-rich, extracellular matrix derived from skeletal muscle, is highly adipogenic in vivo and in vitro

Background/Aims Biological and synthetic scaffolds play important roles in tissue engineering and are being developed towards human clinical applications. Based on previous work from our laboratory, we propose that extracellular matrices from skeletal muscle could be developed for adipose tissue engineering. Methods Extracellular matrices (Myogels) extracted from skeletal muscle of various species were assessed using biochemical assays including ELISA and Western blotting. Biofunctionality was assessed using an in vitro differentiation assay and a tissue engineering construct model in the rat. Results Myogels were successfully extracted from mice, rats, pigs and humans. Myogels contained significant levels of laminin α4- and α2-subunits and collagen I compared to Matrigel™, which contains laminin 1 (α1β1γ1) and collagen IV. Levels of growth factors such as fibroblast growth factor 2 were significantly higher than Matrigel, vascular endothelial growth factor-A levels were significantly lower and all other growth factors were comparable. Myogels reproducibly stimulated adipogenic differentiation of preadipocytes in vitro and the growth of adipose tissue in the rat. Conclusions We found Myogel induces adipocyte differentiation in vitroand shows strong adipogenic potential in vivo, inducing the growth of well-vascularised adipose tissue. Myogel offers an alternative for current support scaffolds in adipose tissue engineering, allowing the scaling up of animal models towards clinical adipose tissue engineering applications.