Tensor reduction for user profiling in personalized recommender systems

User profiling is the process of constructing user models which represent personal characteristics and preferences of customers. User profiles play a central role in many recommender systems. Recommender systems recommend items to users based on user profiles, in which the items can be any objects which the users are interested in, such as documents, web pages, books, movies, etc. In recent years, multidimensional data are getting more and more attention for creating better recommender systems from both academia and industry. Additional metadata provides algorithms with more details for better understanding the interactions between users and items. However, most of the existing user/item profiling techniques for multidimensional data analyze data through splitting the multidimensional relations, which causes information loss of the multidimensionality. In this paper, we propose a user profiling approach using a tensor reduction algorithm, which we will show is based on a Tucker2 model. The proposed profiling approach incorporates latent interactions between all dimensions into user profiles, which significantly benefits the quality of neighborhood formation. We further propose to integrate the profiling approach into neighborhoodbased collaborative filtering recommender algorithms. Experimental results show significant improvements in terms of recommendation accuracy.

Criminal profiling

One of the more widely recognized and practiced subspecialities within forensic criminology is that of criminal profiling. It has a long history, as detailed in Turvey (2008a). It also boasts a small library of distinct literature, with different methods and subspecialities all its own. Criminal profiling is a practice that has seen increasing popular and media attention over the past several decades. It has been depicted in popular fiction such as films like Silence of the Lambs (1991) and television programs like Criminal Minds (2005– present). It has also been applied in a number of high profile cases, including the “Washington Snipers” (see Turvey and McGrath, 2005, for an extended discussion of profiling and the media in the D.C. Sniper case). As a result, students of criminology commonly express an interest in studying criminal profiling with a view to becoming profilers themselves.

Criminal profiling : behavioural consistency, the homology assumption and case linkage

Criminal profiling is an investigative tool used around the world to infer the personality and behavioural characteristics of an offender based on their crime. Case linkage, the process of determining discreet connections between crimes of the same offender, is a practice that falls under the general banner of criminal profiling and has been widely criticized. Two theories, behavioural consistency and the homology assumption, are examined and their impact on profiling in general and case linkage specifically is discussed...

Profiling unlicensed drivers and riders in Queensland. Report to Queensland Department of Transport and Main Roads

Unlicensed driving remains a serious problem for road safety, despite ongoing improvements in traffic law enforcement practices and technology. This report examines de-identified traffic infringement and sanction histories for drivers in Queensland who had lost their licence between 1st January 2003 and 31st December 2008. A total of 546,117 Queensland drivers were identified. Key areas discussed include the prevalence of unlicensed driving and the extent to which particular offences were detected amongst drivers with a licence sanction or disqualified licence.

Who are our repeating students? Profiling students persisting after failure

Research on attrition has focused on the economic significance of low graduation rates in terms of costs to students (fees that do not culminate in a credential) and impact on future income. For a student who fails a unit and repeats the unit multiple times, the financial impact is significant and lasting (Bexley, Daroesman, Arkoudis & James 2013). There are obvious advantages for the timely completion of a degree, both for the student and the institution. Advantages to students include fee minimisation, enhanced engagement opportunities, effectual pathway to employment and a sense of worth, morale and cohort-identity benefits. Work undertaken by the QUT Analytics Project in 2013 and 2014 explored student engagement patterns capturing a variety of data sources and specifically, the use of LMS amongst students in 804 undergraduate units in one semester. Units with high failure rates were given further attention and it was found that students who were repeating a unit were less likely to pass the unit than students attempting it for the first time. In this repeating cohort, academic and behavioural variables were consistently more significant in the modelling than were any demographic variables, indicating that a student’s performance at university is far more impacted by what they do once they arrive than it is by where they come from. The aim of this poster session is to examine the findings and commonalities of a number of case studies that articulated the engagement activities of repeating students (which included collating data from Individual Unit Reports, academic and peer advising programs and engagement with virtual learning resources). Understanding the profile of the repeating student cohort is therefore as important as considering the characteristics of successful students so that the institution might be better placed to target the repeating students and make proactive interventions as early as possible.

Importance of literature profiling: An archival analysis with illustrative examples for IS researchers

The importance of a thorough and systematic literature review has long been recognised across academic domains as critical to the foundation of new knowledge and theory evolution. Driven by an exponentially growing body of knowledge in the IS discipline, there has been a recent influx of guidance on how to conduct a literature review. As literature reviews are emerging as a standalone research method in itself, increasingly these method focused guidelines are of great interest, receiving acceptance at top tier IS publication outlets. Nevertheless, the finer details which offer justification for the selected content, and the effective presentation of supporting data has not been widely discussed in these method papers to date. This paper addresses this gap by exploring the concept of ‘literature profiling’ while arguing that it is a key aspect of a comprehensive literature review. The study establishes the importance of profiling for managing aspects such as quality assurance, transparency and the mitigation of selection bias. And then discusses how profiling can provide a valid basis for data analysis based on the attributes of selected literature. In essence, this study has conducted an archival analysis of literature (predominately from the IS domain) to present its main argument; the value for literature profiling, with supporting exemplary illustrations.

Architecture for service profiling

Service oriented architecture is gaining momentum. However, in order to be successful, the proper and up-to-date description of services is required. Such a description may be provided by service profiling mechanisms, such as one presented in this article. Service profile can be defined as an up-to-date description of a subset of non-functional properties of a service. It allows for service comparison on the basis of non-functional parameters, and choosing the service which is most suited to the needs of a user. In this article the notion of a service profile along with service profiling mechanism is presented as well as the architecture of a profiling system. © 2006 IEEE.

Expression profiling in spondyloarthropathy synovial biopsies highlights changes in expression of inflammatory genes in conjunction with tissue remodelling genes

Background: In the spondyloarthropathies, the underlying molecular and cellular pathways driving disease are poorly understood. By undertaking a study in knee synovial biopsies from spondyloarthropathy (SpA) and ankylosing spondylitis (AS) patients we aimed to elucidate dysregulated genes and pathways. Methods RNA was extracted from six SpA, two AS, three osteoarthritis (OA) and four normal control knee synovial biopsies. Whole genome expression profiling was undertaken using the Illumina DASL system, which assays 24000 cDNA probes. Differentially expressed candidate genes were then validated using quantitative PCR and immunohistochemistry. Results: Four hundred and sixteen differentially expressed genes were identified that clearly delineated between AS/SpA and control groups. Pathway analysis showed altered gene-expression in oxidoreductase activity, B-cell associated, matrix catabolic, and metabolic pathways. Altered «myogene» profiling was also identified. The inflammatory mediator, MMP3, was strongly upregulated (5-fold) in AS/SpA samples and the Wnt pathway inhibitors DKK3 (2.7-fold) and Kremen1 (1.5-fold) were downregulated. Conclusions: Altered expression profiling in SpA and AS samples demonstrates that disease pathogenesis is associated with both systemic inflammation as well as local tissue alterations that may underlie tissue damaging modelling and remodelling outcomes. This supports the hypothesis that initial systemic inflammation in spondyloarthropathies transfers to and persists in the local joint environment, and might subsequently mediate changes in genes directly involved in the destructive tissue remodelling.

Gene expression profiling reveals a downregulation in immune-associated genes in patients with AS

Objective: To identify differentially expressed genes in peripheral blood mononuclear cells (PBMCs) from patients with ankylosing spondylitis (AS) compared with healthy individuals. Methods: RNA was extracted from PBMCs collected from 18 patients with active disease and 18 gender-matched and age-matched controls. Expression profiles of these cells were determined using microarray. Candidate genes with differential expressions were confirmed in the same samples using quantitative reverse transcription-PCR (qRT-PCR). These genes were then validated in a different sample cohort of 35 patients with AS and 18 controls by qRT-PCR. Results: Microarray analysis identified 452 genes detected with 485 probes which were differentially expressed between patients with AS and controls. Underexpression of NR4A2, tumour necrosis factor AIP3 (TNFAIP3) and CD69 was confirmed. These genes were further validated in a different sample group in which the patients with AS had a wider range of disease activity. Predictive algorithms were also developed from the expression data using receiver-operating characteristic curves, which demonstrated that the three candidate genes have ∼80% power to predict AS according to their expression levels. Conclusions: The findings show differences in global gene expression patterns between patients with AS and controls, suggesting an immunosuppressive phenotype in the patients. Furthermore, downregulated expression of three immune-related genes was confirmed. These candidate genes were also shown to be strong predictive markers for AS.

Whole blood transcriptional profiling in ankylosing spondylitis identifies novel candidate genes that might contribute to the inflammatory and tissue-destructive disease aspects

Introduction: A number of genetic-association studies have identified genes contributing to ankylosing spondylitis (AS) susceptibility but such approaches provide little information as to the gene activity changes occurring during the disease process. Transcriptional profiling generates a 'snapshot' of the sampled cells' activity and thus can provide insights into the molecular processes driving the disease process. We undertook a whole-genome microarray approach to identify candidate genes associated with AS and validated these gene-expression changes in a larger sample cohort. Methods: A total of 18 active AS patients, classified according to the New York criteria, and 18 gender- and age-matched controls were profiled using Illumina HT-12 whole-genome expression BeadChips which carry cDNAs for 48,000 genes and transcripts. Class comparison analysis identified a number of differentially expressed candidate genes. These candidate genes were then validated in a larger cohort using qPCR-based TaqMan low density arrays (TLDAs). Results: A total of 239 probes corresponding to 221 genes were identified as being significantly different between patients and controls with a P-value <0.0005 (80% confidence level of false discovery rate). Forty-seven genes were then selected for validation studies, using the TLDAs. Thirteen of these genes were validated in the second patient cohort with 12 downregulated 1.3- to 2-fold and only 1 upregulated (1.6-fold). Among a number of identified genes with well-documented inflammatory roles we also validated genes that might be of great interest to the understanding of AS progression such as SPOCK2 (osteonectin) and EP300, which modulate cartilage and bone metabolism. Conclusions: We have validated a gene expression signature for AS from whole blood and identified strong candidate genes that may play roles in both the inflammatory and joint destruction aspects of the disease.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Expression Profiling of Nucleotide Metabolism-Related Genes in Human Breast Cancer Cells After Treatment with 5-Fluorouracil

5-Fluorouracil (5-FU) is one of the most widely used drugs for treatment of cancers, including breast cancer that exhibits its anticancer activity by inhibiting DNA synthesis and also incorporated into DNA and RNA. The objective of this investigation was to find out the total nucleotide metabolism genes regulated by 5-FU in breast cancer cell line. The breast cancer cell line MCF-7 was treated with the drug 5-FU. To analyze the expression of genes, we have conducted the experiment using 1.7k and 19k human microarray slide and confirmed the expression of genes by semiquantitative reverse transcription-polymerase chain reaction. The expression of 44 genes involved in the nucleotide metabolism pathway was quantified. Of these 44 genes analyzed, transcription of 6 genes were upregulated and 9 genes were downregulated. Earlier studies revealed that the transcription of genes for key enzymes like thymidylate synthase, thymidinekinase, and dihydropyrimidine dehydrogenase are regulated by 5-FU. This study identified some novel genes like thioredoxin reductase, ectonucleotide triphosphate dephosphorylase, and CTP synthase are regulated by 5-FU. The data also reveal large-scale perturbation in transcription of genes not involved directly in the known mechanism of action of 5-FU.

Gene expression profiling of cuticular proteins across the moult cycle of the crab Portunus pelagicus

Background: Crustaceans represent an attractive model to study biomineralization and cuticle matrix formation, as these events are precisely timed to occur at certain stages of the moult cycle. Moulting, the process by which crustaceans shed their exoskeleton, involves the partial breakdown of the old exoskeleton and the synthesis of a new cuticle. This cuticle is subdivided into layers, some of which become calcified while others remain uncalcified. The cuticle matrix consists of many different proteins that confer the physical properties, such as pliability, of the exoskeleton. Results: We have used a custom cDNA microarray chip, developed for the blue swimmer crab Portunus pelagicus, to generate expression profiles of genes involved in exoskeletal formation across the moult cycle. A total of 21 distinct moult-cycle related differentially expressed transcripts representing crustacean cuticular proteins were isolated. Of these, 13 contained copies of the cuticle_1 domain previously isolated from calcified regions of the crustacean exoskeleton, four transcripts contained a chitin_bind_4 domain (RR consensus sequence) associated with both the calcified and un-calcified cuticle of crustaceans, and four transcripts contained an unannotated domain (PfamB_109992) previously isolated from C. pagurus. Additionally, cryptocyanin, a hemolymph protein involved in cuticle synthesis and structural integrity, also displays differential expression related to the moult cycle. Moult stage-specific expression analysis of these transcripts revealed that differential gene expression occurs both among transcripts containing the same domain and among transcripts containing different domains. Conclusion: The large variety of genes associated with cuticle formation, and their differential expression across the crustacean moult cycle, point to the complexity of the processes associated with cuticle formation and hardening. This study provides a molecular entry path into the investigation of the gene networks associated with cuticle formation.

Differential expression profiling of components associated with exoskeletal hardening in crustaceans

Background: Exoskeletal hardening in crustaceans can be attributed to mineralization and sclerotization of the organic matrix. Glycoproteins have been implicated in the calcification process of many matrices. Sclerotization, on the other hand, is catalysed by phenoloxidases, which also play a role in melanization and the immunological response in arthropods. Custom cDNA microarrays from Portunus pelagicus were used to identify genes possibly associated with the activation pathways involved in these processes. Results: Two genes potentially involved in the recognition of glycosylation, the C-type lectin receptor and the mannose-binding protein, were found to display molt cycle-related differential expression profiles. C-type lectin receptor up-regulation was found to coincide with periods associated with new uncalcified cuticle formation, while the up-regulation of mannose-binding protein occurred only in the post-molt stage, during which calcification takes place, implicating both in the regulation of calcification. Genes presumed to be involved in the phenoloxidase activation pathway that facilitates sclerotization also displayed molt cycle-related differential expression profiles. Members of the serine protease superfamily, trypsin-like and chymotrypsin-like, were up-regulated in the intermolt stage when compared to post-molt, while trypsin-like was also up-regulated in pre-molt compared to ecdysis. Additionally, up-regulation in pre- and intermolt stages was observed by transcripts encoding other phenoloxidase activators including the putative antibacterial protein carcinin-like, and clotting protein precursor-like. Furthermore, hemocyanin, itself with phenoloxidase activity, displayed an identical expression pattern to that of the phenoloxidase activators, i.e. up-regulation in pre- and intermolt. Conclusion: Cuticle hardening in crustaceans is a complex process that is precisely timed to occur in the post-molt stage of the molt cycle. We have identified differential expression patterns of several genes that are believed to be involved in biomineralization and sclerotization and propose possible regulatory mechanisms for these processes based on their expression profiles, such as the potential involvement of C-type lectin receptors and mannose binding protein in the regulation of calcification.

Self-Assembly of Bile Steroid Analogues: Molecules, Fibers, and Networks

Structural and rheological features of a series of molecular hydrogels formed by synthetic bile salt analogues have been scrutinized. Among seven gelators, two are neutral compounds, while the others are cationic systems among which one is a tripodal steroid derivative. Despite the fact that the chemical structures are closely related, the variety of physical characteristics is extremely large in the structures of the connected fibers (either plain cylinders or ribbons), in the dynamical modes for stress relaxation of the associated SAFINs, in the scaling laws of the shear elasticity (typical of either cellular solids or fractal floc-like assemblies), in the micron-scale texture and the distribution of ordered domains (spherulites, crystallites) embedded in a random mesh, in the type of nodal zones (either crystalline-like, fiber entanglements, or bundles), in the evolution of the distribution and morphology of fibers and nodes, and in the sensitivity to added salt. SANS appears to be a suitable technique to infer all geometrical parameters defining the fibers, their interaction modes, and the volume fraction of nodes in a SAFIN. The tripodal system is particularly singular in the series and exhibits viscosity overshoots at the startup of shear flows, an “umbrella-like” molecular packing mode involving three molecules per cross section of fiber, and scattering correlation peaks revealing the ordering and overlap of 1d self-assembled polyelectrolyte species.