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Alkylphenol polyethoxylates (APEs) are widely used as nonionic surfactants. Nonylphenol (NP), one of the derivatives of APEs, has been found in the aquatic environment in ranges from nanograms per liter to milligrams per liter. In this study, juvenile rainbow trout were exposed to 0 (control), 66, 220, or 660 micro g NP/L for up to 28 days. Fish remained healthy under NP exposures of 0, 66, and 220 micro g/L for the length of the experiment. All fish died after 4 days of exposure to 660 micro g NP/L. Time-dependent NP bioaccumulation was detected in the tissues of fish exposed to 220 micro g NP/L (P<0.05) and histopathological changes were observed in the livers of fish exposed to 220 micro g NP/L. Furthermore, an increase in the activity of glutathione-S-transferase (GST) was found in the liver of fish exposed to 220 micro g NP/L for 1 week (P<0.05). There was an increase in GST activity in the liver of fish exposed to 66 micro g NP/L but it did not occur before 2 weeks of exposure to NP. The GST activity then decreased in a time-dependent manner in treatment groups, and this decrease was lower in the livers of fish treated with 66 and 220 micro g NP/L than in control fish after 3 weeks of exposure (P<0.05). These results indicated that sublethal doses of NP were accumulating in the bodies of the fish and causing histopathological and biochemical changes in the livers of rainbow trout.

It is generally accepted that the permanent arrest of cell division known as cellular senescence contributes to aging by an antagonistic pleiotropy mechanism: cellular senescence would act beneficially early in life by suppressing cancer, but detrimentally later on by causing frailty and, paradoxically, cancer. In this review, we show that there is room to rethink this common view. We propose a critical appraisal of the arguments commonly brought in support of it, and we qualitatively analyse published results that are of relevance to understand whether or not cellular senescence-associated genes really act in an antagonistic-pleiotropic manner in humans.

The treatment of high-grade tumors must consider a tumor environment dominated by cells that support cancer growth. In addition to directing angiogenesis and invasion, alternatively activated macrophages in the tumor provide protection from adaptive immunity and permit tumor growth. Agonist antibodies to the TNF receptor family member OX40 are an effective therapy for cancer in a range of murine models; however, as with many immune therapies, αOX40 therapy is less effective as the tumor grows and develops an immune suppressive environment. We demonstrate that αOX40 directly activates T cells and that this T cell activation alters macrophage differentiation in the tumor environment. We demonstrate that macrophages in the tumor limit the efficacy of αOX40 therapy, and that combining αOX40 therapy with inhibitors of arginase significantly enhances survival of tumor-bearing mice. These data demonstrate that macrophages in the tumor environment limit the effectiveness of OX40-based immunotherapy, and combination therapies that target both the cell-mediated immune response and the suppressive tumor environment will be required for translation of effective immunotherapies to patients with established tumors. © 2012 The Authors. Immunology © 2012 Blackwell Publishing Ltd, Immunology.

Dynamic Causal Modelling (DCM) was introduced to study the effective connectivity among brain regions using neuroimaging data. Until recently, DCM relied on deterministic models of distributed neuronal responses to external perturbation (e.g., sensory stimulation or task demands). However, accounting for stochastic fluctuations in neuronal activity and their interaction with task-specific processes may be of particular importance for studying state-dependent interactions. Furthermore, allowing for random neuronal fluctuations may render DCM more robust to model misspecification and finesse problems with network identification. In this article, we examine stochastic dynamic causal models (sDCM) in relation to their deterministic counterparts (dDCM) and highlight questions that can only be addressed with sDCM. We also compare the network identification performance of deterministic and stochastic DCM, using Monte Carlo simulations and an empirical case study of absence epilepsy. For example, our results demonstrate that stochastic DCM can exploit the modelling of neural noise to discriminate between direct and mediated connections. We conclude with a discussion of the added value and limitations of sDCM, in relation to its deterministic homologue.

It should not be surprising that a protein with a name like RACK1 - short for Receptor for Activated C Kinase 1 - is found in a variety of signaling complexes. Its alternative name, the splendidly unmemorable GNB2L1 - short for Guanine Nucleotide-binding protein subunit Beta-2-Like 1 - should reinforce this link to signaling complexes. There are currently over 400 publications listed in PubMed mentioning RACK1/GNB2L1 in the abstract, so it is certainly an actively studied protein with much involvement in different aspects of cell regulation is being reported. RACK1 binds to the 40S ribosomal subunit, suggesting it links cell regulation and translation. It is also a target of intracellular parasites. And yet does this protein have the profile that it should? And why are there two kinds of RACK1 researcher who don't seem to communicate well?

Electro- and magnetoencephalography (EEG and MEG) are non-invasive modalities for studying the electrical activity of the brain by measuring voltages on the scalp and magnetic fields outside the head. In the forward problem of EEG and MEG, the relationship between the neural sources and resulting signals is characterized using electromagnetic field theory. This forward problem is commonly solved with the boundary-element method (BEM). The EEG forward problem is numerically challenging due to the low relative conductivity of the skull. In this work, we revise the isolated source approach (ISA) that enables the accurate, computationally efficient BEM solution of this problem. The ISA is formulated for generic basis and weight functions that enable the use of Galerkin weighting. The implementation of the ISA-formulated linear Galerkin BEM (LGISA) is first verified in spherical geometry. Then, the LGISA is compared with conventional Galerkin and symmetric BEM approaches in a realistic 3-shell EEG/MEG model. The results show that the LGISA is a state-of-the-art method for EEG/MEG forward modeling: the ISA formulation increases the accuracy and decreases the computational load. Contrary to some earlier studies, the results show that the ISA increases the accuracy also in the computation of magnetic fields.