The shared allelic architecture of adiponectin levels and coronary artery disease.

OBJECTIVE: A large body of epidemiologic data strongly suggests an association between excess adiposity and coronary artery disease (CAD). Low adiponectin levels, a hormone secreted only from adipocytes, have been associated with an increased risk of CAD in observational studies. However, these associations cannot clarify whether this relationship is causal or due to a shared set of causal factors or even confounding. Genome-wide association studies have identified common variants that influence adiponectin levels, providing valuable tools to examine the genetic relationship between adiponectin and CAD. METHODS: Using 145 genome wide significant SNPs for adiponectin from the ADIPOGen consortium (n = 49,891), we tested whether adiponectin-decreasing alleles influenced risk of CAD in the CARDIoGRAM consortium (n = 85,274). RESULTS: In single-SNP analysis, 5 variants among 145 SNPs were associated with increased risk of CAD after correcting for multiple testing (P < 4.4 × 10(-4)). Using a multi-SNP genotypic risk score to test whether adiponectin levels and CAD have a shared genetic etiology, we found that adiponectin-decreasing alleles increased risk of CAD (P = 5.4 × 10(-7)). CONCLUSION: These findings demonstrate that adiponectin levels and CAD have a shared allelic architecture and provide rationale to undertake a Mendelian randomization studies to understand if this relationship is causal.

Defining the role of common variation in the genomic and biological architecture of adult human height.

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.

Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.

Expression of PROKR1 and PROKR2 in Human Enteric Neural Precursor Cells and Identification of Sequence Variants Suggest a Role in HSCR

Background. The enteric nervous system (ENS) is entirely derived from neural crest and its normal development is regulated by specific molecular pathways. Failure in complete ENS formation results in aganglionic gut conditions such as Hirschsprung's disease (HSCR). Recently, PROKR1 expression has been demonstrated in mouse enteric neural crest derived cells and Prok-1 was shown to work coordinately with GDNF in the development of the ENS. Principal Findings. In the present report, ENS progenitors were isolated and characterized from the ganglionic gut from children diagnosed with and without HSCR, and the expression of prokineticin receptors was examined. Immunocytochemical analysis of neurosphere-forming cells demonstrated that both PROKR1 and PROKR2 were present in human enteric neural crest cells. In addition, we also performed a mutational analysis of PROKR1, PROKR2, PROK1 and PROK2 genes in a cohort of HSCR patients, evaluating them for the first time as susceptibility genes for the disease. Several missense variants were detected, most of them affecting highly conserved amino acid residues of the protein and located in functional domains of both receptors, which suggests a possible deleterious effect in their biological function. Conclusions. Our results suggest that not only PROKR1, but also PROKR2 might mediate a complementary signalling to the RET/GFRα1/GDNF pathway supporting proliferation/survival and differentiation of precursor cells during ENS development. These findings, together with the detection of sequence variants in PROKR1, PROK1 and PROKR2 genes associated to HSCR and, in some cases in combination with RET or GDNF mutations, provide the first evidence to consider them as susceptibility genes for HSCR.

Tumor architecture exerts no bias on nuclear grading in breast cancer diagnosis.

We recently reported that nuclear grading in prostate cancer is subject to a strong confirmation bias induced by the tumor architecture. We now wondered whether a similar bias governs nuclear grading in breast carcinoma. An unannounced test was performed at a pathology conference. Pathologists were asked to grade nuclei in a PowerPoint presentation. Circular high power fields of 27 invasive ductal carcinomas were shown, superimposed over low power background images of either tubule-rich or tubule-poor carcinomas. We found (a) that diagnostic reproducibility of nuclear grades was poor to moderate (weighed kappa values between 0.07 and 0.54, 27 cases, 44 graders), but (b) that nuclear grades were not affected by the tumor architecture. We speculate that the categorized grading in breast cancer, separating tubule formation, nuclear pleomorphism, and mitotic figure counts in a combined three tier score, prevents the bias that architecture exerts on nuclear grades in less well-controlled situations.

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

VLSI architecture for an Underwater Robot Vision System

It is well known that image processing requires a huge amount of computation, mainly at low level processing where the algorithms are dealing with a great number of data-pixel. One of the solutions to estimate motions involves detection of the correspondences between two images. For normalised correlation criteria, previous experiments shown that the result is not altered in presence of nonuniform illumination. Usually, hardware for motion estimation has been limited to simple correlation criteria. The main goal of this paper is to propose a VLSI architecture for motion estimation using a matching criteria more complex than Sum of Absolute Differences (SAD) criteria. Today hardware devices provide many facilities for the integration of more and more complex designs as well as the possibility to easily communicate with general purpose processors

Neural Correlates of the Severity of Cocaine, Heroin, Alcohol, MDMA and Cannabis Use in Polysubstance Abusers: A Resting-PET Brain Metabolism Study

INTRODUCTION Functional imaging studies of addiction following protracted abstinence have not been systematically conducted to look at the associations between severity of use of different drugs and brain dysfunction. Findings from such studies may be relevant to implement specific interventions for treatment. The aim of this study was to examine the association between resting-state regional brain metabolism (measured with 18F-fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and the severity of use of cocaine, heroin, alcohol, MDMA and cannabis in a sample of polysubstance users with prolonged abstinence from all drugs used. METHODS Our sample consisted of 49 polysubstance users enrolled in residential treatment. We conducted correlation analyses between estimates of use of cocaine, heroin, alcohol, MDMA and cannabis and brain metabolism (BM) (using Statistical Parametric Mapping voxel-based (VB) whole-brain analyses). In all correlation analyses conducted for each of the drugs we controlled for the co-abuse of the other drugs used. RESULTS The analysis showed significant negative correlations between severity of heroin, alcohol, MDMA and cannabis use and BM in the dorsolateral prefrontal cortex (DLPFC) and temporal cortex. Alcohol use was further associated with lower metabolism in frontal premotor cortex and putamen, and stimulants use with parietal cortex. CONCLUSIONS Duration of use of different drugs negatively correlated with overlapping regions in the DLPFC, whereas severity of cocaine, heroin and alcohol use selectively impact parietal, temporal, and frontal-premotor/basal ganglia regions respectively. The knowledge of these associations could be useful in the clinical practice since different brain alterations have been associated with different patterns of execution that may affect the rehabilitation of these patients.

Proposal of a parallel architecture for a motion detection algorithm

This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that image processing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed

On AUV control architecture

This paper surveys control architectures proposed in the literature and describes a control architecture that is being developed for a semi-autonomous underwater vehicle for intervention missions (SAUVIM) at the University of Hawaii. Conceived as hybrid, this architecture has been organized in three layers: planning, control and execution. The mission is planned with a sequence of subgoals. Each subgoal has a related task supervisor responsible for arranging a set of pre-programmed task modules in order to achieve the subgoal. Task modules are the key concept of the architecture. They are the main building blocks and can be dynamically re-arranged by the task supervisor. In our architecture, deliberation takes place at the planning layer while reaction is dealt through the parallel execution of the task modules. Hence, the system presents both a hierarchical and an heterarchical decomposition, being able to show a predictable response while keeping rapid reactivity to the dynamic environment

All-Optical Label Stacking: Easing the Trade-offs Between Routing and Architecture Cost in All-Optical Packet Switching

All-optical label swapping (AOLS) forms a key technology towards the implementation of all-optical packet switching nodes (AOPS) for the future optical Internet. The capital expenditures of the deployment of AOLS increases with the size of the label spaces (i.e. the number of used labels), since a special optical device is needed for each recognized label on every node. Label space sizes are affected by the way in which demands are routed. For instance, while shortest-path routing leads to the usage of fewer labels but high link utilization, minimum interference routing leads to the opposite. This paper studies all-optical label stacking (AOLStack), which is an extension of the AOLS architecture. AOLStack aims at reducing label spaces while easing the compromise with link utilization. In this paper, an integer lineal program is proposed with the objective of analyzing the softening of the aforementioned trade-off due to AOLStack. Furthermore, a heuristic aiming at finding good solutions in polynomial-time is proposed as well. Simulation results show that AOLStack either a) reduces the label spaces with a low increase in the link utilization or, similarly, b) uses better the residual bandwidth to decrease the number of labels even more

The ALK-F1174L activating mutation is tumorigenic in MONC-1 neural crest stem cells in an orthotopic murine model of neuroblastoma

Background: Activating mutations of the anaplastic lymphoma receptor tyrosine kinase gene (ALK) were identified in both somatic and familial neuroblastoma. The most common somatic mutation, F1174L, is associated with NMYC amplification and displayed an efficient transforming activity in vivo. In addition, both AKL-F1174L and NMYC were shown cooperate in neuroblastoma tumorigenesis in animal models. To analyse the role of ALK mutations in the oncogenesis of neuroblastoma, ALK wt and various ALK mutants were transduced in murine neural crest stem cells (MONC1). Methods: ALK-wt, and F1174L, and R1275Q mutants were stably expressed by retroviral infection using the pMIGR1 vector in the murine neural crest stem cell line MONC-1, previously immortalised with v-myc, and further implanted subcutaneously or orthotopically in nude mice. Results: Both MONC1-ALK-F1174L and -R1275Q cells displayed a rapid tumour forming capacity upon subcutaneous injection in nude mice compared to control MONC1-MIGR or MONC1 cells. Interestingly, the transforming capacity of the F1174L mutant was much more potent compared to that of R1275Q mutant in murine neural crest stem cells, while ALK-wt was not tumorigenic. In addition, mice implanted orthotopically in the left adrenal gland with MONC1-ALK-F1174L cells developed highly aggressive tumours in 100% of mice within three weeks, while MONC1-Migr or MONC1 derived tumours displayed a longer latency and a reduced tumour take. Conclusions: The activating ALK-F1174L mutant is highly tumorigenic in neural crest stem cells. Nevertheless, we cannot exclude a functional implication of the v-myc oncogene used for MONC1 cells immortalisation. Indeed, the control MONC1-Migr and MONC1 cells were also able to derive subcutaneous and orthotopic tumours, although with considerable reduced efficiency. Further investigations using neural crest stem cell lacking exogenous myc expression are currently on way to assess the exclusive role of ALK mutations in NB oncogenesis.

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The nervous system is a frequent target of industrial chemicals, pharmaceuticals, and environmental pollutants. To screen large numbers of compounds for their neurotoxic potential, in vitro systems are required which combine organ-specific traits with robustness and high reproducibility. These requirements are met by serum-free aggregating brain cell cultures derived from mechanically dissociated embryonic rat brain. The initial cell suspension, composed of neural stem cells, neural progenitor cells, immature postmitotic neurons, glioblasts, and microglial cells, is kept under continuous gyratory agitation. Spherical aggregates form spontaneously and are maintained in suspension culture for several weeks. Within the aggregates, the cells rearrange and mature, reproducing critical morphogenic events such as migration, proliferation, differentiation, synaptogenesis, and myelination. In addition to the spontaneous reconstitution of histotypic brain architecture, the cultures acquire organ-specific functionality as indicated by activity-dependent glucose consumption, spontaneous electrical activity, and brain-specific inflammatory responses. These three-dimensional primary cell cultures offer therefore a unique model for neurotoxicity testing both during development and at advanced cellular differentiation. The high number of aggregates available and the excellent reproducibility of the cultures facilitate routine test procedures. This chapter presents a detailed description of the preparation and maintenance of these cultures as well as their use for routine toxicity testing.