5 research outputs found
Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer’s disease
Funder: Medical Research Council; doi: http://dx.doi.org/10.13039/501100000265Funder: Alzheimer's research ukFunder: Alzheimer's societyFunder: InnomedFunder: National Institute for Health Research; doi: http://dx.doi.org/10.13039/501100000272Abstract: Background: Both serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer’s disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls. Methods: Metabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations. Results: Results revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced observations in serum, but not urine. Conclusions: Urine and serum serotonin concentrations were found to be significantly lower in AD compared with controls, suggesting the bioavailability of the neurotransmitter may be altered in the disease. A significant increase in the kynurenine/tryptophan ratio suggests that this may be a result of a shift to the kynurenine metabolic route due to increased IDO activity, potentially as a result of systemic inflammation. Modulation of the pathways could help improve serotonin bioavailability and signalling in AD patients
On the Principles of Differentiable Quantum Programming Languages
Variational Quantum Circuits (VQCs), or the so-called quantum
neural-networks, are predicted to be one of the most important near-term
quantum applications, not only because of their similar promises as classical
neural-networks, but also because of their feasibility on near-term noisy
intermediate-size quantum (NISQ) machines. The need for gradient information in
the training procedure of VQC applications has stimulated the development of
auto-differentiation techniques for quantum circuits. We propose the first
formalization of this technique, not only in the context of quantum circuits
but also for imperative quantum programs (e.g., with controls), inspired by the
success of differentiable programming languages in classical machine learning.
In particular, we overcome a few unique difficulties caused by exotic quantum
features (such as quantum no-cloning) and provide a rigorous formulation of
differentiation applied to bounded-loop imperative quantum programs, its
code-transformation rules, as well as a sound logic to reason about their
correctness. Moreover, we have implemented our code transformation in OCaml and
demonstrated the resource-efficiency of our scheme both analytically and
empirically. We also conduct a case study of training a VQC instance with
controls, which shows the advantage of our scheme over existing
auto-differentiation for quantum circuits without controls.Comment: Codes are available at https://github.com/LibertasSpZ/adcompil
Quantum approaches to graph colouring
AbstractIn this paper, we investigate quantum algorithms for graph colouring problems, in particular for 2- and 3-colouring of graphs. Our main goal is to establish a set of quantum representations and operations suitable for the problem at hand. We propose unitary- as well as measurement-based quantum computations, also taking inspiration from answer set programming, a form of declarative programming close to traditional logic programming. The approach used is one in which we first generate arbitrary solutions to the problem, then constraining these according to the problem’s input. Though we do not achieve fundamental speed-ups, our algorithms show how quantum concepts can be used for programming and moreover exhibit structural differences. For example, we compute all possible colourings at the same time. We compare our algorithms with classical ones, highlighting how the same type of difficulties give rise to NP-complete behaviour, and propose possible improvements