Remodeling of DNA replication structures by the branch point translocase FANCM

Fanconi anemia (FA) is a genetically heterogeneous chromosome instability syndrome associated with congenital abnormalities, bone marrow failure, and cancer predisposition. Eight FA proteins form a nuclear core complex, which promotes tolerance of DNA lesions in S phase, but the underlying mechanisms are still elusive. We reported recently that the FA core complex protein FANCM can translocate Holliday junctions. Here we show that FANCM promotes reversal of model replication forks via concerted displacement and annealing of the nascent and parental DNA strands. Fork reversal by FANCM also occurs when the lagging strand template is partially single-stranded and bound by RPA. The combined fork reversal and branch migration activities of FANCM lead to extensive regression of model replication forks. These observations provide evidence that FANCM can remodel replication fork structures and suggest a mechanism by which FANCM could promote DNA damage tolerance in S phase

A realistic Computed Tomography simulator for small motion analysis of cerebral aneurysms.

This paper describes a realistic simulator for the Computed Tomography (CT) scan process for motion analysis. In fact, we are currently developing a new framework to find small motion from the CT scan. In order to prove the fidelity of this framework, or potentially any other algorithm, we present in this paper a simulator to simulate the whole CT acquisition process with a priori known parameters. In other words, it is a digital phantom for the motion analysis that can be used to compare the results of any related algorithm with the ground-truth realistic analytical model. Such a simulator can be used by the community to test different algorithms in the biomedical imaging domain. The most important features of this simulator are its different considerations to simulate the best the real acquisition process and its generality.

Sustained NKG2D engagement induces cross-tolerance of multiple distinct NK cell activation pathways

NKG2D is a multisubunit activation receptor that allows natural killer (NK) cells to detect and eliminate stressed, infected, and transformed host cells. However, the chronic exposure of NK cells to cell-bound NKG2D ligands has been shown to impair NKG2D function both in vitro and in vivo. Here we have tested whether continuous NKG2D engagement selectively impacted NKG2D function or whether heterologous NK cell activation pathways were also affected. We found that sustained NKG2D engagement induced cross-tolerization of several unrelated NK cell activation receptors. We show that receptors that activate NK cells via the DAP12/KARAP and DAP10 signaling adaptors, such as murine NKG2D and Ly49D, cross-tolerize preferentially NK cell activation pathways that function independent of DAP10/12, such as antibody-dependent cell-mediated cytotoxicity and missing-self recognition. Conversely, DAP10/12-independent pathways are unable to cross-tolerize unrelated NK cell activation receptors such as NKG2D or Ly49D. These data define a class of NK cell activation receptors that can tolerize mature NK cells. The reversible suppression of the NK cells' cytolytic function probably reduces the NK cells' efficacy to control endogenous and exogenous stress yet may be needed to limit tissue damage

T-cell receptor V beta use predicts reactivity and tolerance to Mlsa-encoded antigens.

T lymphocytes reactive with the product of the Mlsa-allele of the minor lymphocyte stimulating (Mls) locus use a predominant T-cell receptor beta-chain variable gene segment (V beta 6). Such V beta 6-bearing T cells are selectively eliminated in the thymus of Mlsa-bearing mice, consistent with a model in which tolerance to self antigens is achieved by clonal deletion.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Predicting trabecular bone microdamage initiation and accumulation using a non-linear perfect damage model

Studies evaluating the mechanical behavior of the trabecular microstructure play an important role in our understanding of pathologies such as osteoporosis, and in increasing our understanding of bone fracture and bone adaptation. Understanding of such behavior in bone is important for predicting and providing early treatment of fractures. The objective of this study is to present a numerical model for studying the initiation and accumulation of trabecular bone microdamage in both the pre- and post-yield regions. A sub-region of human vertebral trabecular bone was analyzed using a uniformly loaded anatomically accurate microstructural three-dimensional finite element model. The evolution of trabecular bone microdamage was governed using a non-linear, modulus reduction, perfect damage approach derived from a generalized plasticity stress-strain law. The model introduced in this paper establishes a history of microdamage evolution in both the pre- and post-yield regions

Transplantation tolerance induced by regulatory T cells: in vivo mechanisms and sites of action.

The mechanisms by which CD4(+)CD25(+)Foxp3(+) T (Treg) cells regulate effector T cells in a transplantation setting and their in vivo homeostasis still remain to be clarified. Using a mouse adoptive transfer model, we analyzed the in vivo expansion, trafficking, and effector function of alloreactive T cells and donor-specific Treg cells, in response to a full-thickness skin allograft. Fluorescent-labeled CD4(+)CD25(-) and antigen-specific Treg cells were transferred alone or co-injected into syngeneic BALB/c-Nude recipients transplanted with skins from (C57BL/6 x BALB/c) F1 donors. Treg cells divided in vivo, migrated and accumulated in the allograft draining lymph nodes as well as within the graft. The co-transfer of Treg cells did not modify the early activation and homing of CD4(+)CD25(-) T cells in secondary lymphoid organs. However, in the presence of Treg cells, alloreactive CD4(+)CD25(-) T cells produced significantly less IFN-gamma and were present in reduced numbers in the secondary lymphoid organs. Furthermore, time-course studies showed that Treg cells were recruited into the allograft at a very early stage after transplantation and effectively prevented the infiltration of effector T cells. In conclusion, suppression of rejection requires the early recruitment to the site of antigenic challenge of donor-specific Treg cells, which then mainly regulate the effector arm of T cell alloresponses.

A peptide inhibitor of C-jun N-terminal kinase modulates hepatic damage and the inflammatory response after hemorrhagic shock and resuscitation

Hemorrhage and resuscitation (H/R) leads to phosphorylation of mitogen-activated stress kinases, an event that is associated with organ damage. Recently, a specific, cell-penetrating, protease-resistant inhibitory peptide of the mitogen-activated protein kinase c-JUN N-terminal kinase (JNK) was developed (D-JNKI-1). Here, using this peptide, we tested if inhibition of JNK protects against organ damage after H/R. Male Sprague-Dawley rats were treated with D-JNKI-1 (11 mg/kg, i.p.) or vehicle. Thirty minutes later, rats were hemorrhaged for 1 h to a MAP of 30 to 35 mmHg and then resuscitated with 60% of the shed blood and twice the shed blood volume as Ringer lactate. Tissues were harvested 2 h later. ANOVA with Tukey post hoc analysis or Kruskal-Wallis ANOVA on ranks, P < 0.05, was considered significant. c-JUN N-terminal kinase inhibition decreased serum alanine aminotransferase activity as a marker of liver injury by 70%, serum creatine kinase activity by 67%, and serum lactate dehydrogenase activity by 60% as compared with vehicle treatment. The histological tissue damage observed was blunted after D-JNKI-1 pretreatment both for necrotic and apoptotic cell death. Hepatic leukocyte infiltration and serum IL-6 levels were largely diminished after D-JNKI-1 pretreatment. The extent of oxidative stress as evaluated by immunohistochemical detection of 4-hydroxynonenal was largely abrogated after JNK inhibition. After JNK inhibition, activation of cJUN after H/R was also reduced. Hemorrhage and resuscitation induces a systemic inflammatory response and leads to end-organ damage. These changes are mediated, at least in part, by JNK. Therefore, JNK inhibition deserves further evaluation as a potential treatment option in patients after resuscitated blood loss.

Tolerance-inducing immunosuppressive strategies in clinical transplantation: an overview.

The significant development of immunosuppressive drug therapies within the past 20 years has had a major impact on the outcome of clinical solid organ transplantation, mainly by decreasing the incidence of acute rejection episodes and improving short-term patient and graft survival. However, long-term results remain relatively disappointing because of chronic allograft dysfunction and patient morbidity or mortality, which is often related to the adverse effects of immunosuppressive treatment. Thus, the induction of specific immunological tolerance of the recipient towards the allograft remains an important objective in transplantation. In this article, we first briefly describe the mechanisms of allograft rejection and immune tolerance. We then review in detail current tolerogenic strategies that could promote central or peripheral tolerance, highlighting the promises as well as the remaining challenges in clinical transplantation. The induction of haematopoietic mixed chimerism could be an approach to induce robust central tolerance, and we describe recent encouraging reports of end-stage kidney disease patients, without concomitant malignancy, who have undergone combined bone marrow and kidney transplantation. We discuss current studies suggesting that, while promoting peripheral transplantation tolerance in preclinical models, induction protocols based on lymphocyte depletion (polyclonal antithymocyte globulins, alemtuzumab) or co-stimulatory blockade (belatacept) should, at the current stage, be considered more as drug-minimization rather than tolerance-inducing strategies. Thus, a better understanding of the mechanisms that promote peripheral tolerance has led to newer approaches and the investigation of individualized donor-specific cellular therapies based on manipulated recipient regulatory T cells.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Bihippocampal damage with emotional dysfunction: impaired auditory recognition of fear.

A right-handed man developed a sudden transient, amnestic syndrome associated with bilateral hemorrhage of the hippocampi, probably due to Urbach-Wiethe disease. In the 3rd month, despite significant hippocampal structural damage on imaging, only a milder degree of retrograde and anterograde amnesia persisted on detailed neuropsychological examination. On systematic testing of recognition of facial and vocal expression of emotion, we found an impairment of the vocal perception of fear, but not that of other emotions, such as joy, sadness and anger. Such selective impairment of fear perception was not present in the recognition of facial expression of emotion. Thus emotional perception varies according to the different aspects of emotions and the different modality of presentation (faces versus voices). This is consistent with the idea that there may be multiple emotion systems. The study of emotional perception in this unique case of bilateral involvement of hippocampus suggests that this structure may play a critical role in the recognition of fear in vocal expression, possibly dissociated from that of other emotions and from that of fear in facial expression. In regard of recent data suggesting that the amygdala is playing a role in the recognition of fear in the auditory as well as in the visual modality this could suggest that the hippocampus may be part of the auditory pathway of fear recognition.

Drug refractory epilepsy in brain damage: effect of dextromethorphan on EEG in four patients.

High doses of dextromethorphan (20-42 mg/kg/day) were given to four critically ill children with seizures and frequent epileptiform abnormalities in the EEG that were refractory to antiepileptic drugs. Their acute diseases (hypoxia, head trauma and hypoxia, neurodegenerative disease, hypoglycaemia) were thought to be due in part to N-methyl-D-aspartate (NMDA) receptor mediated processes. Treatment with dextromethorphan, an NMDA receptor antagonist, was started between 48 hours and 14 days after the critical incident. In three patients the EEG improved considerably within 48 hours and seizures ceased within 72 hours. In the patient with neurodegenerative disease the effect on the EEG was impressive, but the seizures were not controlled. Despite the improvement of the EEG the clinical outcome was poor in all children: three died in the critical period or due to the progressing disease; the patient with hypoglycaemia survived with severe neurological sequelae. Plasma concentrations of dextromethorphan varied between 74-1730 ng/ml and its metabolite dextrorphan varied between 349-3790 ng/ml. In one patient corresponding concentrations in CSF were lower than those in plasma. The suppression of epileptic discharges by the doses of dextromethorphan given suggests that such doses are sufficient to block NMDA receptors.

Segmentation of brain structures in presence of a space-occupying lesion.

Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.