Real Time Imaging of Biomarkers in the Parkinson\u27s Brain Using Mini-Implantable Biosensors. II. Pharmaceutical Therapy with Bromocriptine

We used Neuromolecular Imaging (NMI) and trademarked BRODERICK PROBE® mini-implantable biosensors, to selectively and separately detect neuro-transmitters in vivo, on line, within seconds in the dorsal striatal brain of the Parkinson’s Disease (PD) animal model. We directly compared our results derived from PD to the normal striatal brain of the non-Parkinson’s Disease (non-PD) animal. This advanced biotechnology enabled the imaging of dopamine (DA), serotonin (5-HT), homovanillic acid (HVA) a metabolite of DA, L-tryptophan (L-TP) a precursor to 5-HT and peptides, dynorphin A 1-17 (Dyn A) and somatostatin (somatostatin releasing inhibitory factor) (SRIF). Each neurotransmitter and neurochemical was imaged at a signature electroactive oxidation/half-wave potential in dorsal striatum of the PD as compared with the non-PD animal. Both endogenous and bromocriptine-treated neurochemical profiles in PD and non-PD were imaged using the same experimental paradigm and detection sensitivities. Results showed that we have found significant neurotransmitter peptide biomarkers in the dorsal striatal brain of endogenous and bromocriptine-treated PD animals. The peptide biomarkers were not imaged in dorsal striatal brain of non-PD animals, either endogenously or bromocriptine-treated. These findings provide new pharmacotherapeutic strategies for PD patients. Thus, our findings are highly applicable to the clinical treatment of PD

Functional Characterisation of Alpha-Galactosidase A Mutations as a Basis for a New Classification System in Fabry Disease

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The study has been supported partially by an unrestricted scientific grant from Shire Human Genetic Therapies (Germany

Rationality as the Rule of Reason

The demands of rationality are linked both to our subjective normative perspective (given that rationality is a person-level concept) and to objective reasons or favoring relations (given that rationality is non-contingently authoritative for us). In this paper, I propose a new way of reconciling the tension between these two aspects: roughly, what rationality requires of us is having the attitudes that correspond to our take on reasons in the light of our evidence, but only if it is competent. I show how this view can account for structural rationality on the assumption that intentions and beliefs as such involve competent perceptions of downstream reasons, and explore various implications of the account

The bottleneck may be the solution, not the problem

As a highly consequential biological trait, a memory \u201cbottleneck\u201d cannot escape selection pressures. It must therefore co-evolve with other cognitive mechanisms rather than act as an independent constraint. Recent theory and an implemented model of language acquisition suggest that a limit on working memory may evolve to help learning. Furthermore, it need not hamper the use of language for communication

CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201

Contrast-Enhanced Magnetic Resonance Imaging Confirmation of an Anterior Protein Pathway in Normal Rabbit Eyes

Purpose: Contrast-enhanced proton magnetic resonance imaging ( 1H MRI) has been used as a quantitative, noninvasive method to corroborate a pathway for the diffusion of plasma-derived protein into the aqueous humor in the normal rabbit eye. Methods. T1-weighted magnetic resonance images were produced over 1- to 3-hour periods after the intravenous injection of gadolinium diethylenetriamine-pentaacetic acid. Results. Analysis of the images yielded the time dependence of signal enhancements within the areas of interest. The ciliary body showed an immediate sharp increase, followed by a gradual decrease in signal enhancement with time. Although a gradual increase in signal enhancement was found in the anterior chamber, no significant change occurred in the posterior chamber. A similar MRI experiment with an owl monkey produced parallel, though smaller, signal enhancements in the ciliary body and anterior chamber. Again, however, no significant change was found in the posterior chamber. Conclusions. These results support and extend those of recent fluorophotometric, tracer-localization, and modeling studies demonstrating that in the normal rabbit and monkey eye, plasma-derived proteins bypass the posterior chamber, entering the anterior chamber directly via the iris root

Floquet engineering of the Lifshitz phase transition in the Hubbard model

Within the Floquet theory of periodically driven quantum systems, we demonstrate that an off-resonant high-frequency electromagnetic field can induce the Lifshitz phase transition in periodical structures described by the one-dimensional repulsive Hubbard model with the nearest and next-nearest neighbor hopping. The transition changes the topology of electron energy spectrum at the Fermi level, transforming it from the two Fermi-points to the four Fermi-points, what facilitates the emergence of the superconducting fluctuations in the structure. Possible manifestations of the effect and conditions of its experimental observability are discussed

Algorithm engineering for optimal alignment of protein structure distance matrices

Protein structural alignment is an important problem in computational biology. In this paper, we present first successes on provably optimal pairwise alignment of protein inter-residue distance matrices, using the popular Dali scoring function. We introduce the structural alignment problem formally, which enables us to express a variety of scoring functions used in previous work as special cases in a unified framework. Further, we propose the first mathematical model for computing optimal structural alignments based on dense inter-residue distance matrices. We therefore reformulate the problem as a special graph problem and give a tight integer linear programming model. We then present algorithm engineering techniques to handle the huge integer linear programs of real-life distance matrix alignment problems. Applying these techniques, we can compute provably optimal Dali alignments for the very first time

Transient amplifiers of selection and reducers of fixation for death-Birth updating on graphs

The spatial structure of an evolving population affects which mutations become fixed. Some structures amplify selection, increasing the likelihood that beneficial mutations become fixed while deleterious mutations do not. Other structures suppress selection, reducing the effect of fitness differences and increasing the role of random chance. This phenomenon can be modeled by representing spatial structure as a graph, with individuals occupying vertices. Births and deaths occur stochastically, according to a specified update rule. We study death-Birth updating: An individual is chosen to die and then its neighbors compete to reproduce into the vacant spot. Previous numerical experiments suggested that amplifiers of selection for this process are either rare or nonexistent. We introduce a perturbative method for this problem for weak selection regime, meaning that mutations have small fitness effects. We show that fixation probability under weak selection can be calculated in terms of the coalescence times of random walks. This result leads naturally to a new definition of effective population size. Using this and other methods, we uncover the first known examples of transient amplifiers of selection (graphs that amplify selection for a particular range of fitness values) for the death-Birth process. We also exhibit new families of "reducers of fixation", which decrease the fixation probability of all mutations, whether beneficial or deleterious.Comment: 51 pages, 5 figure