N-Benzyl-5-methoxytryptamines as Potent Serotonin 5-HT2 Receptor Family Agonists and Comparison with a Series of Phenethylarnine Analogues

A series of N-benzylated-5-methoxytryptamine analogues was prepared and investigated, with special emphasis on substituents in the meta position of the benzyl group. A parallel series of several N-benzylated analogues of 2,5- dimethoxy-4-iodophenethylamine (2C-I) also was included for comparison of the two major templates (i.e., tryptamine and phenethylamine). A broad affinity screen at serotonin receptors showed that most of the compounds had the highest affinity at the 5-HT2 family receptors. Substitution at the para position of the benzyl group resulted in reduced affinity, whereas substitution in either the ortho or the meta position enhanced affinity. In general, introduction of a large lipophilic group improved affinity, whereas functional activity often followed the opposite trend. Tests of the compounds for functional activity utilized intracellular Ca2+ mobilization. Function was measured at the human 5-HT2A, 5-HT2B, and 5-HT2C receptors, as well as at the rat 5-HT2A and 5-HT2C receptors. There was no general correlation between affinity and function. Several of the tryptamine congeners were very potent functionally (EC50 values from 7.6 to 63 nM), but most were partial agonists. Tests in the mouse head twitch assay revealed that many of the compounds induced the head

Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms

We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context. For simplicity, we choose the kernel Fischer Linear Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters in an Expectation-Maximization framework, we define model fidelity via the hinge loss of the decision function. The resulting algorithm optimizes the parameters of the LDDMM norm-inducing differential operator as a solution to a group-wise registration and classification problem. In practice, this may lead to a biology-aware registration, focusing its attention on the predictive task at hand such as identifying the effects of disease. We first tested our algorithm on a synthetic dataset, showing that our parameter selection improves registration quality and classification accuracy. We then tested the algorithm on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our Schizophrenia-Control predictive model showed significant improvement in ROC AUC compared to baseline parameters

Unsupervised Deformable Image Registration Using Cycle-Consistent CNN

Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to the classical approaches. In this paper, we present a novel unsupervised medical image registration method that trains deep neural network for deformable registration of 3D volumes using a cycle-consistency. Thanks to the cycle consistency, the proposed deep neural networks can take diverse pair of image data with severe deformation for accurate registration. Experimental results using multiphase liver CT images demonstrate that our method provides very precise 3D image registration within a few seconds, resulting in more accurate cancer size estimation.Comment: accepted for MICCAI 201

De facto exchange rate regime classifications: an evaluation

There exist several statistically-based exchange rate regime classifications that disagree with one another to a disappointing degree. To what extent is this a matter of the quality of the design of these schemes, and to what extent does it reflect the need to supplement statistics with other information (as is done in the IMF’s de facto classification)? It is shown that statistical methods are good at the basics (distinguishing some type of peg from some type of float), but less helpful in other respects, such as determining whether a float is managed, particularly for countries that are not very remote from their main trading partners. Different measures of exchange rate volatility have been used but are not primarily responsible for differences between classifications. The theoretical underpinning of particular classification schemes needs to be more explicit

Graceful degradation under noise on brain inspired robot controllers

How can we build robot controllers that are able to work under harsh conditions, but without experiencing catastrophic failures? As seen on the recent Fukushima’s nuclear disaster, standard robots break down when exposed to high radiation environments. Here we present the results from two arrangements of Spiking Neural Networks, based on the Liquid State Machine (LSM) framework, that were able to gracefully degrade under the effects of a noisy current injected directly into each simulated neuron. These noisy currents could be seen, in a simplified way, as the consequences of exposition to non-destructive radiation. The results show that not only can the systems withstand noise, but one of the configurations, the Modular Parallel LSM, actually improved its results, in a certain range, when the noise levels were increased. Also, the robot controllers implemented in this work are suitable to run on a modern, power efficient neuromorphic hardware such as SpiNNaker

Psychosocial risk factors and retinal microvascular signs: the multi-ethnic study of atherosclerosis

The association between psychosocial risk factors and retinal microvascular signs was examined in the Multi-Ethnic Study of Atherosclerosis. Subjects were recruited from Baltimore, Maryland; Chicago, Illinois; Forsyth County, North Carolina; Los Angeles County, California; New York, New York; and St. Paul, Minnesota. Levels of depressive symptoms, trait anger, trait anxiety, chronic burdens, emotional support, and cynical distrust were assessed by questionnaire (from July 2000 to July 2002). Digital retinal images (from August 2002 to January 2004) from 6,147 participants were used to evaluate retinopathy and retinal vascular caliber. After controlling for potential confounding factors, the authors found that subjects without access to emotional support (Enriched Social Support Instrument score of or = 19) had 60% greater odds of retinopathy (odds ratio = 1.6, 95% confidence interval (CI): 1.3, 2.0). Subjects with high Spielberger trait-anxiety scale scores (> or = 22 vs. or = 16 vs. <16) were also more likely to have retinopathy (odds ratio = 1.4, 95% CI: 1.1, 1.9 and odds ratio = 1.5, 95% CI: 1.2, 1.9), respectively. In this cross-sectional study, lack of emotional support, increased trait anxiety, and more depressive symptoms were associated with retinopathy signs, independently of other known risk factors.http://deepblue.lib.umich.edu/bitstream/2027.42/78377/1/JensenShea2010_AJE.pd

TRAP1 chaperone protein mutations and autoinflammation

We identified a consanguineous kindred, of three affected children with severe autoinflammation, resulting in the death of one sibling and allogeneic stem cell transplantation in the other two. All three were homozygous for MEFV p.S208C mutation; however, their phenotype was more severe than previously reported, prompting consideration of an oligogenic autoinflammation model. Further genetic studies revealed homozygous mutations in TRAP1, encoding the mitochondrial/ER resident chaperone protein tumour necrosis factor receptor associated protein 1 (TRAP1). Identification of a fourth, unrelated patient with autoinflammation and compound heterozygous mutation of TRAP1 alone facilitated further functional studies, confirming the importance of this protein as a chaperone of misfolded proteins with loss of function, which may contribute to autoinflammation. Impaired TRAP1 function leads to cellular stress and elevated levels of serum IL-18. This study emphasizes the importance of considering digenic or oligogenic models of disease in particularly severe phenotypes and suggests that autoinflammatory disease might be enhanced by bi-allelic mutations in TRAP1

Epstein-Barr Virus Reactivation After Paediatric Haematopoietic Stem Cell Transplantation: Risk Factors and Sensitivity Analysis of Mathematical Model

Epstein-Barr virus (EBV) establishes a lifelong latent infection in healthy humans, kept under immune control by cytotoxic T cells (CTLs). Following paediatric haematopoetic stem cell transplantation (HSCT), a loss of immune surveillance leads to opportunistic outgrowth of EBV-infected cells, resulting in EBV reactivation, which can ultimately progress to post-transplant lymphoproliferative disorder (PTLD). The aims of this study were to identify risk factors for EBV reactivation in children in the first 100 days post-HSCT and to assess the suitability of a previously reported mathematical model to mechanistically model EBV reactivation kinetics in this cohort. Retrospective electronic data were collected from 56 children who underwent HSCT at Great Ormond Street Hospital (GOSH) between 2005 and 2016. Using EBV viral load (VL) measurements from weekly quantitative PCR (qPCR) monitoring post-HSCT, a multivariable Cox proportional hazards (Cox-PH) model was developed to assess time to first EBV reactivation event in the first 100 days post-HSCT. Sensitivity analysis of a previously reported mathematical model was performed to identify key parameters affecting EBV VL. Cox-PH modelling revealed EBV seropositivity of the HSCT recipient and administration of anti-thymocyte globulin (ATG) pre-HSCT to be significantly associated with an increased risk of EBV reactivation in the first 100 days post-HSCT (adjusted hazard ratio (AHR) = 2.32, P = 0.02; AHR = 2.55, P = 0.04). Five parameters were found to affect EBV VL in sensitivity analysis of the previously reported mathematical model. In conclusion, we have assessed the effect of multiple covariates on EBV reactivation in the first 100 days post-HSCT in children and have identified key parameters in a previously reported mechanistic mathematical model that affect EBV VL. Future work will aim to fit this model to patient EBV VLs, develop the model to account for interindividual variability and model the effect of clinically relevant covariates such as rituximab therapy and ATG on EBV VL

Strengthening fairness, transparency and accountability in health care priority setting at district level in Tanzania

Health care systems are faced with the challenge of resource scarcity and have insufficient resources to respond to all health problems and target groups simultaneously. Hence, priority setting is an inevitable aspect of every health system. However, priority setting is complex and difficult because the process is frequently influenced by political, institutional and managerial factors that are not considered by conventional priority-setting tools. In a five-year EU-supported project, which started in 2006, ways of strengthening fairness and accountability in priority setting in district health management were studied. This review is based on a PhD thesis that aimed to analyse health care organisation and management systems, and explore the potential and challenges of implementing Accountability for Reasonableness (A4R) approach to priority setting in Tanzania. A qualitative case study in Mbarali district formed the basis of exploring the sociopolitical and institutional contexts within which health care decision making takes place. The study also explores how the A4R intervention was shaped, enabled and constrained by the contexts. Key informant interviews were conducted. Relevant documents were also gathered and group priority-setting processes in the district were observed. The study revealed that, despite the obvious national rhetoric on decentralisation, actual practice in the district involved little community participation. The assumption that devolution to local government promotes transparency, accountability and community participation, is far from reality. The study also found that while the A4R approach was perceived to be helpful in strengthening transparency, accountability and stakeholder engagement, integrating the innovation into the district health system was challenging. This study underscores the idea that greater involvement and accountability among local actors may increase the legitimacy and fairness of priority-setting decisions. A broader and more detailed analysis of health system elements, and socio-cultural context is imperative in fostering sustainability. Additionally, the study stresses the need to deal with power asymmetries among various actors in priority-setting contexts