Deciphering past and present atmospheric metal pollution of urban environments: The role of black crusts formed on historical constructions

Construction materials affected by black crusts (BCs) can be subjected to restoration, demolition, recycling or even to their management as waste products. Therefore, the determination of their chemical features should be considered a crucial step before undertaking any action. In this work, we present the development of an analytical methodology useful to be implemented as a routine screening tool to detect recent and past atmospheric emissions of heavy metals, nowadays superficially deposited or even encapsulated in BCs. For its development, BCs together with the underneath original substrate/construction material were sampled from the historical construction Punta Begona Galleries (Getxo, Basque Country, North of Spain). In order to detect quickly and in a cost-effective way the stratification of the metallic deposits in the BCs over time (surface or external/recent and internal/past), thin sections were analyzed by elemental spectroscopic imaging techniques (SEM-EDS and mu-ED-XRF). In the external part of the BCs, iron particles were mainly identified, whereas in the inner areas (past deposition events) of the most exposed BCs to the atmosphere, lead accumulations together with zinc and copper were identified. Additional Raman imaging studies allowed to perform the molecular speciation study of lead, identifying mainly laurionite (PbClOH) together with hydrocerussite (Pb-3(CO3)(2)(OH)(2)). The presence of the mentioned lead chloride hydroxide confirms the role of the marine aerosol (chloride input) in the formation of the metallic compounds. These experimental evidences were used to assist the chemical equilibrium models developed to explain the reactivity pathway, which lead to the formation of the identified compounds. Through ICP-MS and lead isotopic ratio analysis, more than 3000 mg kg(-1) of lead were quantified in the BCs, probably coming from the old emissions conducted by the old power station close to the construction,. That lead content can be high enough to consider those crusts as a source of metallic contamination and a possible risk to the environment and human health

Automatic business process model extension to repair constraint violations

Consider an artifact-centric business process model, containing both a data model and a process model. When executing the process, it may happen that some of the data constraints from the data model are violated. Bearing this in mind, we propose an approach to automatically generate an extension to the original business process model that, when executed after a constraint violation, repairs the contents of the data leaving it in a new consistent state.Peer ReviewedPostprint (author's final draft

Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics

The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p = 0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83% of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products

Myofibromatosis: imaging characteristics

Background : Infantile myofibromatosis is the most common fibrous tumor of infancy. It can involve the skin, muscle, bone, and viscera. This uncommon entity is subdivided into solitary and multicentric forms, with or without visceral involvement. Objective : To describe the imaging characteristics of extracranial myofibromatosis. Materials and methods : Six infants, aged 1 day–1 week, were evaluated by imaging. All six patients had evaluation of one of the masses by US; four patients had CT evaluation of at least one of the masses; and five patients had evaluation by MRI. Results : The US appearance of the myofibromas included a mass with a purely anechoic center with a thick wall, a mass with a partially anechoic center, and a mass without anechoic components. On enhanced CT, the masses had lower or similar attenuation compared to adjacent muscle, with some masses exhibiting peripheral enhancement. The MR appearance consisted of low signal on T1-weighted imaging. On T2-weighted imaging, two had low signal of the center and the other three had high signal. All masses showed peripheral enhancement after gadolinium administration. Conclusions : Myofibromas have variable appearance on US, with a mass with an anechoic center being the most common feature. On CT, the mass can exhibit peripheral enhancement, calcifications, and erosion of adjacent bone. The MR appearance consisted of low signal on T1-weighted imaging and high or low signal of the center on T2-weighted imaging. All masses showed peripheral enhancement after gadolinium administration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46718/1/247_2004_Article_1357.pd

Kinesin Light Chain 1 Suppression Impairs Human Embryonic Stem Cell Neural Differentiation and Amyloid Precursor Protein Metabolism

The etiology of sporadic Alzheimer disease (AD) is largely unknown, although evidence implicates the pathological hallmark molecules amyloid beta (Aβ) and phosphorylated Tau. Work in animal models suggests that altered axonal transport caused by Kinesin-1 dysfunction perturbs levels of both Aβ and phosphorylated Tau in neural tissues, but the relevance of Kinesin-1 dependent functions to the human disease is unknown. To begin to address this issue, we generated human embryonic stem cells (hESC) expressing reduced levels of the kinesin light chain 1 (KLC1) Kinesin-1 subunit to use as a source of human neural cultures. Despite reduction of KLC1, undifferentiated hESC exhibited apparently normal colony morphology and pluripotency marker expression. Differentiated neural cultures derived from KLC1-suppressed hESC contained neural rosettes but further differentiation revealed obvious morphological changes along with reduced levels of microtubule-associated neural proteins, including Tau and less secreted Aβ, supporting the previously established connection between KLC1, Tau and Aβ. Intriguingly, KLC1-suppressed neural precursors (NPs), isolated using a cell surface marker signature known to identify cells that give rise to neurons and glia, unlike control cells, failed to proliferate. We suggest that KLC1 is required for normal human neural differentiation, ensuring proper metabolism of AD-associated molecules APP and Tau and for proliferation of NPs. Because impaired APP metabolism is linked to AD, this human cell culture model system will not only be a useful tool for understanding the role of KLC1 in regulating the production, transport and turnover of APP and Tau in neurons, but also in defining the essential function(s) of KLC1 in NPs and their progeny. This knowledge should have important implications for human neurodevelopmental and neurodegenerative diseases

Identification of CIITA Regulated Genetic Module Dedicated for Antigen Presentation

The class II trans-activator CIITA is a transcriptional co-activator required for the expression of Major Histocompatibility Complex (MHC) genes. Although the latter function is well established, the global target-gene specificity of CIITA had not been defined. We therefore generated a comprehensive list of its target genes by performing genome-wide scans employing four different approaches designed to identify promoters that are occupied by CIITA in two key antigen presenting cells, B cells and dendritic cells. Surprisingly, in addition to MHC genes, only nine new targets were identified and validated by extensive functional and expression analysis. Seven of these genes are known or likely to function in processes contributing to MHC-mediated antigen presentation. The remaining two are of unknown function. CIITA is thus uniquely dedicated for genes implicated in antigen presentation. The finding that CIITA regulates such a highly focused gene expression module sets it apart from all other transcription factors, for which large-scale binding-site mapping has indicated that they exert pleiotropic functions and regulate large numbers of genes

Inferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networks

Background: Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches. Methodology/Principal Findings: Here we report a simple algorithm that asks "which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?" It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1), glycolysis (HLF), mitochondrial transcription (TFB2M), adipogenesis (PIAS1), neuronal development (TLX3), immune function (IRF1) and vasculogenesis (SOX17), within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C) were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal) to 30 months post natal (adulthood) for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a 'metabolic axis' formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes. Conclusions/Significance: The new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo transcriptional regulation of skeletal muscle development

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.</p> <p>Results</p> <p>This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.</p> <p>The algorithm is evaluated with synthetic and <it>Saccharomyces cerevisiae </it>expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.</p> <p>The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and <it>Saccharomyces cerevisiae </it>expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.</p> <p>Conclusion</p> <p>From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.</p