Abstract

As legacy toxicogenomics databases have become available, improved data mining approaches are now key to extracting and visualizing subtle relationships between toxicants and gene expression. In the present study, a novel “aggregating bundles of clusters” (ABC) procedure was applied to separate cholestatic from non-cholestatic drugs and model toxicants in the Johnson & Johnson (Janssen) rat liver toxicogenomics database [3] . Drug-induced cholestasis is an important issue, particularly when a new compound enters the market with this liability, with standard preclinical models often mispredicting this toxicity. Three well-characterized cholestasis-responsive genes (Cyp7a1, Mrp3 and Bsep) were chosen from a previous in-house Janssen gene expression signature, these three genes show differing, non-redundant responses across the 90+ paradigm compounds in our database. Using the ABC procedure, extraneous contributions were minimized in comparisons of compound gene responses. All genes were assigned weights proportional to their correlations with Cyp7a1, Mrp3 and Bsep, and a resampling technique was used to derive a stable measure of compound similarity. The compounds that were known to be associated with rat cholestasis generally had small values of this measure relative to each other but also had large values of this measure relative to non-cholestatic compounds. Visualization of the data with the ABC-derived signature showed a very tight, essentially identically behaving cluster of robust human cholestatic drugs and experimental cholestatic toxicants (ethinyl estradiol, LPS, ANIT and methylene dianiline, disulfiram, naltrexone, methapyrilene, phenacetin, alpha-methyl dopa, flutamide, the NSAIDs–—indomethacin, flurbiprofen, diclofenac, flufenamic acid, sulindac, and nimesulide, butylated hydroxytoluene, piperonyl butoxide, and bromobenzene), some slightly less active compounds (3′-acetamidofluorene, amsacrine, hydralazine, tannic acid), some drugs that behaved very differently, and were distinct from both non-cholestatic and cholestatic drugs (ketoconazole, dipyridamole, cyproheptadine and aniline), and many postulated human cholestatic drugs that in rat showed no evidence of cholestasis (chlorpromazine, erythromycin, niacin, captopril, dapsone, rifampicin, glibenclamide, simvastatin, furosemide, tamoxifen, and sulfamethoxazole). Most of these latter drugs were noted previously by other groups as showing cholestasis only in humans. The results of this work suggest that the ABC procedure and similar statistical approaches can be instrumental in combining data to compare toxicants across toxicogenomics databases, extract similarities among responses and reduce unexplained data varation.

Abstract

The results of a study of social networking by professors’ co-memberships in library and information science PhD juries between 1993 and 2006 are presented. Degree reduction and k-core methods are used to extract sub-networks and a Ward’s hierarchical cluster analysis is performed. The resulting network identifies five classes, whose members are actors occupying similar social positions, and at the same time different mechanisms, based mainly in similarity and physical proximity, are proposed from the interpretative point of view.

Abstract

Many scientific applications deal with data from a multitude of different sources, e.g., measurements, imaging and simulations. Each source provides an additional perspective on the phenomenon of interest, but also comes with specific limitations, e.g. regarding accuracy, spatial and temporal availability. Effectively combining and analyzing such multimodal and partially incomplete data of limited accuracy in an integrated way is challenging. In this work, we outline an approach for an integrated analysis and visualization of the atmospheric impact of volcano eruptions. The data sets comprise observation and imaging data from satellites as well as results from numerical particle simulations. To analyze the clouds from the volcano eruption in the spatiotemporal domain we apply topological methods. We show that topology-related extremal structures of the data support clustering and comparison. We further discuss the robustness of those methods with respect to different properties of the data and different parameter setups. Finally we outline open challenges for the effective integrated visualization using topological methods. Document type: Part of book or chapter of book

Abstract

Self-healing within asphalt pavements is the process whereby road cracks can be repaired automatically when thermal and mechanical conditions are met. To accelerate and improve this healing process, metal particles are added to asphalt mixtures. However, this approach is costly both in economic and environmental terms due to the use of virgin metallic particles. So, even though the self-healing of asphalt mixtures has been widely addressed in experimental terms over the years, there is a lack of research aimed at modelling this phenomenon, especially with the purpose of optimizing the use of metal particles through the valorization of industrial by-products. As such, the goal of this study was to develop a statistical methodology to model the healing capacity of asphalt concrete mixtures (AC-16) from the characteristics of the metal particles added and the time and intensity used for magnetic induction. Five metal particles were used as heating inductors, including four types of industrial by-products aimed at transforming waste products into material for use in the road sector. The proposed approach consisted of a combination of cluster algorithms, multiple regression analysis and response optimization, which were applied to model laboratory data obtained after testing asphalt concrete mixtures containing these inductors. The results proved the accuracy of the statistical methods used to reproduce the experimental behaviour of the asphalt mixtures, which enabled the authors to determine the optimal amount of industrial by-products and time needed to make the self-healing process as efficient as possible.

Abstract

As an important province in the southeast coastal area, Fujian is composed of 9 cities. Due to various economic, political and geographical influences, the logistics development of these cities is uneven. Based on the analysis of the factors affecting the competitiveness of urban logistics, this paper establishes the evaluation index system of urban logistics competitiveness. Factor analysis is used to evaluate the logistics competitiveness index of each city in Fujian Province, and the comprehensive score and ranking of the logistics competitiveness of each city are obtained. Cluster analysis is used to divide 9 cities in Fujian into 4 categories, and the reasons for the results are analyzed. Document type: Article

Abstract

Background: This study aims to examine to what extent sustainability has been incorporated into assessments of road infrastructure projects. It identifies promising approaches that include indicators reflecting core sustainability criteria, determines criteria that were insufficiently covered as indicators, and develops an integrated indicator set covering all criteria. Methods: A systematic review was performed to obtain all related papers/reports in two academic databases: Scopus and Web of Sciences. The indicators extracted from papers/reports were first coded, then evaluated by using quantitative and qualitative content analysis. Results: The project appraisal methods for decision-making is found to be a promising approach, covering more extensive criteria than others. Two criteria – namely adaptation and precaution and intergenerational equity – were hardly ever adopted as indicators. Ten main groups of indicators were extracted to construct an integrated set incorporating all core criteria. Conclusions: Some criteria appear to have become mainstream, while others deserve attention. The safest choice is to combine methods/tools or to adopt the integrated set developed for exhaustive criteria inclusion. Document type: Article

Abstract

Once open data initiatives are consolidated worldwide, it is important to find out if the Spanish regional open data portals are useful for active transparency. To this end, we developed an indicator to assess the existence of information and its reusable quality in three periods of time (2013, 2015 and 2018). Then, we inquired about the existence of differentiated groups of regional governments using, when necessary, cluster analysis. We found indications that there are two conglomerates of governments: advanced and lagged. These findings are in line with those offered by two opposing views on the dissemination of information from the public sector: agency and neo-institutional theories.

Abstract

Since the beginning of the 21st century, an increasing number of Chinese researchers have joined the ranks of the world’s top scientists. Some international organizations have observed this phenomenon and ranked the world’s top Chinese researchers. However, investigation of highly cited interdisciplinary research (IDR) scholars is insufficient, although IDR tends to have a greater social impact. Looking at the top 2% of the world’s Chinese scholars, this study analyzes the structural attributes of IDR by those top scholars in detail using network analysis, cluster analysis, block modeling, and quadratic assignment procedure analysis. The results show that the proportion of highly cited scholars in technical categories is higher than in social categories. The fields of artificial intelligence and image processing, oncology and carcinogenesis, plus neurology and neurosurgery serve as bridges across disciplines, with materials, energy, and artificial intelligence and image processing having higher eigenvector centrality. The field of social sciences has the widest range of IDR activities, but cooperation within this field is low. Forty-two of the world’s first-class universities are in China, and of the world’s top 2% scholars who come from China, 46.3% work for these institutions. The research themes of highly cited academics from World First-Class universities in China are most similar to the themes of scholars from universities in China with first-class academic disciplines. There are differences between non-university and university scholars in terms of research topics. It is suggested that the government can promote a triple-helix effect (public institute, industrial enterprise, and research school) so that organizations of different natures can produce synergistic effects.  

Abstract

To meet China’s CO2 intensity target of 40%–45% reduction by 2020 based on the 2005 level, a regional allocation method based on cluster analysis is developed. Thirty Chinese provinces are classified into six groups based on economy, emissions, and reduction potential indicators. Under the equity principle, the two most developed groups are assigned the highest reduction targets (55% and 65%, respectively). However, their reduction potential is limited. Under the efficiency principle, the two groups with the highest reduction potential take the highest targets (48% and 61%, respectively), but their economy is relatively backward. When equity and efficiency are equally weighted, the 5th group with a prominent reduction potential takes the highest target (54%), and the 2nd and the 3rd groups with large industry scales take the second highest target (49%). However, under all the three allocation schemes, the targets are not greater than 40% for the 4th and the 6th groups, which have a relatively low economic ability, emissions, and reduction potential. Due to inconsistency between economic and reduction potential, corresponding market mechanisms and policy instruments should be established to ensure equity and efficiency of regional target allocation. Yang, Y., W.-J. Cai, C. Wang, et al., 2012: Regional allocation of CO2 intensity reduction targets based on cluster analysis. Adv. Clim. Change Res., 3 (4), doi: 10.3724/SP.J.1248.2012.00220.