Interference of endocrine disrupting chemicals with aromatase CYP19 expression or activity, and consequences for reproduction of teleost fish

Many natural and synthetic compounds present in the environment exert a number of adverse effects on the exposed organisms, leading to endocrine disruption, for which they were termed endocrine disrupting chemicals (EDCs). A decrease in reproduction success is one of the most well-documented signs of endocrine disruption in fish. Estrogens are steroid hormones involved in the control of important reproduction-related processes, including sexual differentiation, maturation and a variety of others. Careful spatial and temporal balance of estrogens in the body is crucial for proper functioning. At the final step of estrogen biosynthesis, cytochrome P450 aromatase, encoded by the cyp19 gene, converts androgens into estrogens. Modulation of aromatase CYP19 expression and function can dramatically alter the rate of estrogen production, disturbing the local and systemic levels of estrogens. In the present review, the current progress in CYP19 characterization in teleost fish is summarized and the potential of several classes of EDCs to interfere with CYP19 expression and activity is discussed. Two cyp19 genes are present in most teleosts, cyp19a and cyp19b, primarily expressed in the ovary and brain, respectively. Both aromatase CYP19 isoforms are involved in the sexual differentiation and regulation of the reproductive cycle and male reproductive behavior in diverse teleost species. Alteration of aromatase CYP19 expression and/or activity, be it upregulation or downregulation, may lead to diverse disturbances of the above mentioned processes. Prediction of multiple transcriptional regulatory elements in the promoters of teleost cyp19 genes suggests the possibility for several EDC classes to affect cyp19 expression on the transcriptional level. These sites include cAMP responsive elements, a steroidogenic factor 1/adrenal 4 binding protein site, an estrogen-responsive element (ERE), half-EREs, dioxin-responsive elements, and elements related to diverse other nuclear receptors (peroxisome proliferator activated receptor, retinoid X receptor, retinoic acid receptor). Certain compounds including phytoestrogens, xenoestrogens, fungicides and organotins may modulate aromatase CYP19 activity on the post-transcriptional level. As is shown in this review, diverse EDCs may affect the expression and/or activity of aromatase cyp19 genes through a variety of mechanisms, many of which need further characterization in order to improve the prediction of risks posed by a contaminated environment to teleost fish population.

Interference during the implicit learning of two different motor sequences

It has been demonstrated that learning a second motor task after having learned a first task may interfere with the long-term consolidation of the first task. However, little is known about immediate changes in the representation of the motor memory in the early acquisition phase within the first minutes of the learning process. Therefore, we investigated such early interference effects with an implicit serial reaction time task in 55 healthy subjects. Each subject performed either a sequence learning task involving two different sequences, or a random control task. The results showed that learning the first sequence led to only a slight, short-lived interference effect in the early acquisition phase of the second sequence. Overall, learning of neither sequence was impaired. Furthermore, the two processes, sequence-unrelated task learning (i.e. general motor training) and the sequence learning itself did not appear to interfere with each other. In conclusion, although the long-term consolidation of a motor memory has been shown to be sensitive to other interfering memories, the present study suggests that the brain is initially able to acquire more than one new motor sequence within a short space of time without significant interference.

RNA interference-mediated c-MYC inhibition prevents cell growth and decreases sensitivity to radio- and chemotherapy in childhood medulloblastoma cells

BACKGROUND: With current treatment strategies, nearly half of all medulloblastoma (MB) patients die from progressive tumors. Accordingly, the identification of novel therapeutic strategies remains a major goal. Deregulation of c-MYC is evident in numerous human cancers. In MB, over-expression of c-MYC has been shown to cause anaplasia and correlate with unfavorable prognosis. METHODS: To study the role of c-MYC in MB biology, we down-regulated c-MYC expression by using small interfering RNA (siRNA) and investigated changes in cellular proliferation, cell cycle analysis, apoptosis, telomere maintenance, and response to ionizing radiation (IR) and chemotherapeutics in a representative panel of human MB cell lines expressing different levels of c-MYC (DAOY wild-type, DAOY transfected with the empty vector, DAOY transfected with c-MYC, D341, and D425). RESULTS: siRNA-mediated c-MYC down-regulation resulted in an inhibition of cellular proliferation and clonogenic growth, inhibition of G1-S phase cell cycle progression, and a decrease in human telomerase reverse transcriptase (hTERT) expression and telomerase activity. On the other hand, down-regulation of c-MYC reduced apoptosis and decreased the sensitivity of human MB cells to IR, cisplatin, and etoposide. This effect was more pronounced in DAOY cells expressing high levels of c-MYC when compared with DAOY wild-type or DAOY cells transfected with the empty vector. CONCLUSION: In human MB cells, in addition to its roles in growth and proliferation, c-MYC is also a potent inducer of apoptosis. Therefore, targeting c-MYC might be of therapeutic benefit when used sequentially with chemo- and radiotherapy rather than concomitantly.

Increased parietal activity after training of interference control

Recent studies suggest that computerized cognitive training leads to improved performance in related but untrained tasks (i.e. transfer effects). However, most study designs prevent disentangling which of the task components are necessary for transfer. In the current study, we examined whether training on two variants of the adaptive dual n-back task would affect untrained task performance and the corresponding electrophysiological event-related potentials (ERPs). Forty three healthy young adults were trained for three weeks with a high or low interference training variant of the dual n-back task, or they were assigned to a passive control group. While n-back training with high interference led to partial improvements in the Attention Network Test (ANT), we did not find transfer to measures of working memory and fluid intelligence. ERP analysis in the n-back task and the ANT indicated overlapping processes in the P3 time range. Moreover, in the ANT, we detected increased parietal activity for the interference training group alone. In contrast, we did not find electrophysiological differences between the low interference training and the control group. These findings suggest that training on an interference control task leads to higher electrophysiological activity in the parietal cortex, which may be related to improvements in processing speed, attentional control, or both.

The bivalency effect represents an interference-triggered adjustment of cognitive control: an ERP study

When bivalent stimuli (i.e., stimuli with relevant features for two different tasks) occur occasionally among univalent stimuli, performance is slowed on subsequent univalent stimuli even if they have no overlapping stimulus features. This finding has been labeled the bivalency effect. It indexes an adjustment of cognitive control, but the underlying mechanism is not well understood yet. The purpose of the present study was to shed light on this question, using event-related potentials. We used a paradigm requiring predictable alternations between three tasks, with bivalent stimuli occasionally occurring on one task. The results revealed that the bivalency effect elicited a sustained parietal positivity and a frontal negativity, a neural signature that is typical for control processes implemented to resolve interference. We suggest that the bivalency effect reflects interference, which may be caused by episodic context binding.

Semantic Methods for Intelligent Distributed Design Environments

Continuous advancements in technology have led to increasingly comprehensive and distributed product development processes while in pursuit of improved products at reduced costs. Information associated with these products is ever changing, and structured frameworks have become integral to managing such fluid information. Ontologies and the Semantic Web have emerged as key alternatives for capturing product knowledge in both a human-readable and computable manner. The primary and conclusive focus of this research is to characterize relationships formed within methodically developed distributed design knowledge frameworks to ultimately provide a pervasive real-time awareness in distributed design processes. Utilizing formal logics in the form of the Semantic Web’s OWL and SWRL, causal relationships are expressed to guide and facilitate knowledge acquisition as well as identify contradictions between knowledge in a knowledge base. To improve the efficiency during both the development and operational phases of these “intelligent” frameworks, a semantic relatedness algorithm is designed specifically to identify and rank underlying relationships within product development processes. After reviewing several semantic relatedness measures, three techniques, including a novel meronomic technique, are combined to create AIERO, the Algorithm for Identifying Engineering Relationships in Ontologies. In determining its applicability and accuracy, AIERO was applied to three separate, independently developed ontologies. The results indicate AIERO is capable of consistently returning relatedness values one would intuitively expect. To assess the effectiveness of AIERO in exposing underlying causal relationships across product development platforms, a case study involving the development of an industry-inspired printed circuit board (PCB) is presented. After instantiating the PCB knowledge base and developing an initial set of rules, FIDOE, the Framework for Intelligent Distributed Ontologies in Engineering, was employed to identify additional causal relationships through extensional relatedness measurements. In a conclusive PCB redesign, the resulting “intelligent” framework demonstrates its ability to pass values between instances, identify inconsistencies amongst instantiated knowledge, and identify conflicting values within product development frameworks. The results highlight how the introduced semantic methods can enhance the current knowledge acquisition, knowledge management, and knowledge validation capabilities of traditional knowledge bases.

Enterprise Integration of Smart Objects using Semantic Service Descriptions

Integrating physical objects (smart objects) and enterprise IT systems is still a labor intensive, mainly manual task done by domain experts. On one hand, enterprise IT backend systems are based on service oriented architectures (SOA) and driven by business rule engines or business process execution engines. Smart objects on the other hand are often programmed at very low levels. In this paper we describe an approach that makes the integration of smart objects with such backends systems easier. We introduce semantic endpoint descriptions based on Linked USDL. Furthermore, we show how different communication patterns can be integrated into these endpoint descriptions. The strength of our endpoint descriptions is that they can be used to automatically create REST or SOAP endpoints for enterprise systems, even if which they are not able to talk to the smart objects directly. We evaluate our proposed solution with CoAP, UDP and 6LoWPAN, as we anticipate the industry converge towards these standards. Nonetheless, our approach also allows easy integration with backend systems, even if no standardized protocol is used.

Integration of WSNs into enterprise systems based on semantic physical business entities

Internet of Things based systems are anticipated to gain widespread use in industrial applications. Standardization efforts, like 6L0WPAN and the Constrained Application Protocol (CoAP) have made the integration of wireless sensor nodes possible using Internet technology and web-like access to data (RESTful service access). While there are still some open issues, the interoperability problem in the lower layers can now be considered solved from an enterprise software vendors' point of view. One possible next step towards integration of real-world objects into enterprise systems and solving the corresponding interoperability problems at higher levels is to use semantic web technologies. We introduce an abstraction of real-world objects, called Semantic Physical Business Entities (SPBE), using Linked Data principles. We show that this abstraction nicely fits into enterprise systems, as SPBEs allow a business object centric view on real-world objects, instead of a pure device centric view. The interdependencies between how currently services in an enterprise system are used and how this can be done in a semantic real-world aware enterprise system are outlined, arguing for the need of semantic services and semantic knowledge repositories. We introduce a lightweight query language, which we use to perform a quantitative analysis of our approach to demonstrate its feasibility.

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

A fuzzy grassroots ontology for improving social semantic web search

The web is continuously evolving into a collection of many data, which results in the interest to collect and merge these data in a meaningful way. Based on that web data, this paper describes the building of an ontology resting on fuzzy clustering techniques. Through continual harvesting folksonomies by web agents, an entire automatic fuzzy grassroots ontology is built. This self-updating ontology can then be used for several practical applications in fields such as web structuring, web searching and web knowledge visualization.A potential application for online reputation analysis, added value and possible future studies are discussed in the conclusion.