Discovery of substituted maleimides as liver X receptor agonists and determination of a ligand-bound crystal structure

Substituted 3-(phenylamino)-1H-pyrrole-2,5-diones were identified from a high throughput screen as inducers of human ATP binding cassette transporter A1 expression. Mechanism of action studies led to the identification of GSK3987 (4) as an LXR ligand. 4 recruits the steroid receptor coactivator-1 to human LXR alpha and LXRP with EC(50)s of 40 nM, profiles as an LXR agonist in functional assays, and activates LXR though a mechanism that is similar to first generation LXR agonists.

Help or hindrance? Supplier involvement and causal ambiguity within inter-organisational NPD teams

Suppliers are increasingly involved in buyer firms’ interorganizational new product development (NPD) teams. Yet the transfer of knowledge within this context may be subject to varying degrees of causal ambiguity, potentially limiting the effect of supplier involvement on performance. We develop a theoretical model exploring the effect of supplier involvement practices on the level of causal ambiguity within interorganizational NPD teams, and the subsequent impact on competitor imitation, new product advantage, and project performance. Our model also serves as a test of the paradox that causal ambiguity both inhibits imitation by competitors, but also adversely affects organisational outcomes. Results from an empirical study of 119 R&D intensive manufacturing firms in the United Kingdom largely support these hypotheses. Results from structural equation modeling show that supplier involvement orientation and long-term commitment lower causal ambiguity within interorganizational NPD teams. In turn, this lower causal ambiguity generates a new product advantage and increases project performance for the buyer firm, but has no significant effect on competitor imitation. Instead, competitor imitation is delayed by the extent to which the firm develops a new product advantage within the market. These results shed light on the causal ambiguity paradox showing that lower causal ambiguity during interorganizational new product development increases both product and project performance, but without reducing barriers to imitation. Product development managers are encouraged to utilize supplier involvement practices to minimise ambiguity in the NPD project, and to target their supplier involvement efforts on solving causally ambiguous technological problems to sustain a competitive advantage.

GALEX AND PAN-STARRS1 DISCOVERY OF SN IIP 2010aq: THE FIRST FEW DAYS AFTER SHOCK BREAKOUT IN A RED SUPERGIANT STAR

We present the early UV and optical light curve of Type IIP supernova (SN) 2010aq at z = 0.0862, and compare it to analytical models for thermal emission following SN shock breakout in a red supergiant star. SN 2010aq was discovered in joint monitoring between the Galaxy Evolution Explorer (GALEX) Time Domain Survey (TDS) in the NUV and the Pan-STARRS1 Medium Deep Survey (PS1 MDS) in the g, r, i, and z bands. The GALEX and Pan-STARRS1 observations detect the SN less than 1 day after the shock breakout, measure a diluted blackbody temperature of 31,000 +/- 6000 K 1 day later, and follow the rise in the UV/optical light curve over the next 2 days caused by the expansion and cooling of the SN ejecta. The high signal-to-noise ratio of the simultaneous UV and optical photometry allows us to fit for a progenitor star radius of 700 +/- 200R(circle dot), the size of a red supergiant star. An excess in UV emission two weeks after shock breakout compared with SNe well fitted by model atmosphere-code synthetic spectra with solar metallicity is best explained by suppressed line blanketing due to a lower metallicity progenitor star in SN 2010aq. Continued monitoring of PS1 MDS fields by the GALEX TDS will increase the sample of early UV detections of Type II SNe by an order of magnitude and probe the diversity of SN progenitor star properties.

Inferring the conservative causal core of gene regulatory networks

Background
Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.

Results
In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.

Conclusions
For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

Discovery of Interstellar Anions in Cepheus and Auriga

We report the detection of microwave emission lines from the hydrocarbon anion C6H- and its parent neutral C6H in the star-forming region L1251A (in Cepheus), and the pre-stellar core L1512 (in Auriga). The carbon-chain-bearing species C4H, HC3N, HC5N, HC7N and C3S are also detected in large abundances. The observations of L1251A constitute the first detections of anions and long- chain polyynes and cyanopolyynes (with more than 5 carbon atoms) in the Cepheus Flare star- forming region, and the first detection of anions in the vicinity of a protostar outside of the Taurus molecular cloud complex, highlighting a wider importance for anions in the chemistry of star formation. Rotational excitation temperatures have been derived from the HC3N hyperfine structure lines, and are found to be 6.2 K for L1251A and 8.7 K for L1512. The anion-to-neutral ratios are 3.6% and 4.1%, respectively, which are within the range of values previously observed in the interstellar medium, and suggest a relative uniformity in the processes governing anion abundances in different dense interstellar clouds. This research contributes towards the growing body of evidence that carbon chain anions are relatively abundant in interstellar clouds throughout the Galaxy, but especially in the regions of relatively high density and high depletion surrounding pre-stellar cores and young, embedded protostars.

TRARESA: a tissue microarray-based hospital system for biomarker validation and discovery

Formalin fixed and paraffin embedded tissue (FFPE) collections in pathology departments are the largest resource for retrospective biomedical research studies. Based on the literature analysis of FFPE related research, as well as our own technical validation, we present the Translational Research Arrays (TRARESA), a tissue microarray centred, hospital based, translational research conceptual framework for both validation and/or discovery of novel biomarkers. TRARESA incorporates the analysis of protein, DNA and RNA in the same samples, correlating with clinical and pathological parameters from each case, and allowing (a) the confirmation of new biomarkers, disease hypotheses and drug targets, and (b) the postulation of novel hypotheses on disease mechanisms and drug targets based on known biomarkers. While presenting TRARESA, we illustrate the use of such a comprehensive approach. The conceptualisation of the role of FFPE-based studies in translational research allows the utilisation of this commodity, and adds to the hypothesis-generating armamentarium of existing high-throughput technologies.

The Colorectal cancer disease-specific transcriptome may facilitate the discovery of more biologically and clinically relevant information

BACKGROUND: To date, there are no clinically reliable predictive markers of response to the current treatment regimens for advanced colorectal cancer. The aim of the current study was to compare and assess the power of transcriptional profiling using a generic microarray and a disease-specific transcriptome-based microarray. We also examined the biological and clinical relevance of the disease-specific transcriptome.

METHODS: DNA microarray profiling was carried out on isogenic sensitive and 5-FU-resistant HCT116 colorectal cancer cell lines using the Affymetrix HG-U133 Plus2.0 array and the Almac Diagnostics Colorectal cancer disease specific Research tool. In addition, DNA microarray profiling was also carried out on pre-treatment metastatic colorectal cancer biopsies using the colorectal cancer disease specific Research tool. The two microarray platforms were compared based on detection of probesets and biological information.

RESULTS: The results demonstrated that the disease-specific transcriptome-based microarray was able to out-perform the generic genomic-based microarray on a number of levels including detection of transcripts and pathway analysis. In addition, the disease-specific microarray contains a high percentage of antisense transcripts and further analysis demonstrated that a number of these exist in sense:antisense pairs. Comparison between cell line models and metastatic CRC patient biopsies further demonstrated that a number of the identified sense:antisense pairs were also detected in CRC patient biopsies, suggesting potential clinical relevance.

CONCLUSIONS: Analysis from our in vitro and clinical experiments has demonstrated that many transcripts exist in sense:antisense pairs including IGF2BP2, which may have a direct regulatory function in the context of colorectal cancer. While the functional relevance of the antisense transcripts has been established by many studies, their functional role is currently unclear; however, the numbers that have been detected by the disease-specific microarray would suggest that they may be important regulatory transcripts. This study has demonstrated the power of a disease-specific transcriptome-based approach and highlighted the potential novel biologically and clinically relevant information that is gained when using such a methodology.

Prognostic and Predictive Biomarkers in Resected Colon Cancer: Current Status and Future Perspectives for Integrating Genomics into Biomarker Discovery

The number of agents that are potentially effective in the adjuvant treatment of locally advanced resectable colon cancer is increasing. Consequently, it is important to ascertain which subgroups of patients will benefit from a specific treatment. Despite more than two decades of research into the molecular genetics of colon cancer, there is a lack of prognostic and predictive molecular biomarkers with proven utility in this setting. A secondary objective of the Pan European Trials in Adjuvant Colon Cancer-3 trial, which compared irinotecan in combination with 5-fluorouracil and leucovorin in the postoperative treatment of stage III and stage II colon cancer patients, was to undertake a translational research study to assess a panel of putative prognostic and predictive markers in a large colon cancer patient cohort. The Cancer and Leukemia Group B 89803 trial, in a similar design, also investigated the use of prognostic and predictive biomarkers in this setting. In this article, the authors, who are coinvestigators from these trials and performed similar investigations of biomarker discovery in the adjuvant treatment of colon cancer, review the current status of biomarker research in this field, drawing on their experiences and considering future strategies for biomarker discovery in the postgenomic era. The Oncologist 2010; 15: 390-404

Diagnostic and prognostic biomarker discovery strategies for autoimmune disorders.

Current clinical, laboratory or radiological parameters cannot accurately diagnose or predict disease outcomes in a range of autoimmune disorders. Biomarkers which can diagnose at an earlier time point, predict outcome or help guide therapeutic strategies in autoimmune diseases could improve clinical management of this broad group of debilitating disorders. Additionally, there is a growing need for a deeper understanding of multi-factorial autoimmune disorders. Proteomic platforms offering a multiplex approach are more likely to reflect the complexity of autoimmune disease processes. Findings from proteomic based studies of three distinct autoimmune diseases are presented and strategies compared. It is the authors' view that such approaches are likely to be fruitful in the movement of autoimmune disease treatment away from reactive decisions and towards a preventative stand point.

The causal attributions of nursing students towards adolescent survivors of brain injury

Background: The hidden nature of brain injury means that it is often difficult for people to understand the sometimes challenging behaviors that individuals exhibit. The misattribution of these behaviors may lead to a lack of consideration and public censure if the individual is seen as simply misbehaving.

Objective: The aim of this study was to explore the impact of visual cues indicating the presence or absence of brain injury on prejudice, desire for social interaction, and causal attributions of nursing and computing science students.

Method: An independent-groups design was employed in this research, which recruited 190 first-year nursing students and 194 first-year computing science students from a major university in Belfast, UK. A short passage describing an adolescent’s behavior after a brain injury, together with one of three images portraying a young adolescent with a scar, a head dressing, or neither of these, was given to participants. They were then asked to answer questions relating to prejudice, social interaction, locus of control, and causal attributions. The attributional statements suggested that the character’s behavior could be the result of brain injury or adolescence.

Results: Analysis of variance demonstrated a statistically significant difference between the student groups, where nursing students (M = 45.17, SD = 4.69) desired more social interaction with the fictional adolescent than their computer science peers (M = 38.64, SD = 7.69). Further, analysis of variance showed a main effect of image on the attributional statement that described adolescence as a suitable explanation for the character’s lack of self-confidence.

Discussion: Attributions of brain injury were influenced by the presence of a visible but potentially specious indicator of injury. This suggests that survivors of brain injury who do not display any outward indicator may receive less care and face expectations to behave in a manner consistent with the norms of society. If their injury does not allow them to meet with these expectations, they may face public censure and discrimination.

A TMA De-Arraying Method for High Throughput Biomarker Discovery in Tissue Research

Background: Tissue MicroArrays (TMAs) represent a potential high-throughput platform for the analysis and discovery of tissue biomarkers. As TMA slides are produced manually and subject to processing and sectioning artefacts, the layout of TMA cores on the final slide and subsequent digital scan (TMA digital slide) is often disturbed making it difficult to associate cores with their original position in the planned TMA map. Additionally, the individual cores can be greatly altered and contain numerous irregularities such as missing cores, grid rotation and stretching. These factors demand the development of a robust method for de-arraying TMAs which identifies each TMA core, and assigns them to their appropriate coordinates on the constructed TMA slide.

Methodology: This study presents a robust TMA de-arraying method consisting of three functional phases: TMA core segmentation, gridding and mapping. The segmentation of TMA cores uses a set of morphological operations to identify each TMA core. Gridding then utilises a Delaunay Triangulation based method to find the row and column indices of each TMA core. Finally, mapping correlates each TMA core from a high resolution TMA whole slide image with its name within a TMAMap.

Conclusion: This study describes a genuine robust TMA de-arraying algorithm for the rapid identification of TMA cores from digital slides. The result of this de-arraying algorithm allows the easy partition of each TMA core for further processing. Based on a test group of 19 TMA slides (3129 cores), 99.84% of cores were segmented successfully, 99.81% of cores were gridded correctly and 99.96% of cores were mapped with their correct names via TMAMaps. The gridding of TMA cores were also extensively tested using a set of 113 pseudo slide (13,536 cores) with a variety of irregular grid layouts including missing cores, rotation and stretching. 100% of the cores were gridded correctly.

Are causal and intervention judgments inextricably linked? A developmental study.

The application of the formal framework of causal Bayesian Networks to children's causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different causal structures. The first two experiments examined whether children's causal structure and intervention judgments were consistent with one another. In Experiment 1, children aged between 4 and 8years made causal structure judgments on a three-component causal system followed by counterfactual intervention judgments. In Experiment 2, children's causal structure judgments were followed by intervention judgments phrased as future hypotheticals. In Experiment 3, we explicitly told children what the correct causal structure was and asked them to make intervention judgments. The results of the three experiments suggest that the representations that support causal structure judgments do not easily support simple judgments about interventions in children. We discuss our findings in light of strong interventionist claims that the two types of judgments should be closely linked. © 2011 Cognitive Science Society, Inc.


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