Qudit state estimation with a fixed set of bases

We analyse the estimation of a pure d-dimensional quantum state with a finite number of measurements and compare several estimation schemes. In this paper we concentrate on consecutive von Neumann measurements on a finite number of identically prepared systems in dimensions d=2, d=4 and d=8. We propose two schemes with different types of fixed measurement directions. Inspired by integration theory our first approach uses the Halton sequence (a so-called quasi-Monte Carlo sequence) to obtain measurement directions (`sampling points') with high uniformity over the configuration space. Our second approach extends this idea and optimises the distribution of the measurement directions to yield a rather high fidelity in quantum state estimation. This optimisation results in a uniform distribution of the directions and large quantum distances between the directions. Furthermore we establish a link to mutually unbiased bases

Automated finite element modelling of 3D woven textiles

The advance of 3D fabric technology allows tailored material structure in different directions for optimised performance. 3D fabrics open up increasing applications in automotive, medical, energy and many other areas. This paper explores highly automated techniques to simulate 3D fabric geometry and mechanical behaviour. The basis of the work starts from TexGen,an open source software package developed at the University of Nottingham. A complex variety of 3D fabrics can be defined as subclass functions from base functions for individual yarns, crosssections and yarn paths. The 3D fabric geometry can be generated automatically in TexGen using a script given a number of parameters. From this geometrical model, an automated procedure is followed to create an input file for finite element analysis. Yarns are meshed with hexahedral and wedge elements. The input file contains the mesh, element orientations, material definitions, contact surfaces and definitions, periodic boundary conditions and loading steps. Once the FEA simulation is completed, the nodal displacement data are parsed into TexGen to build the deformed 3D fabric geometry. The deformed geometry model can then be used for further analyses, eg. to predict permeability or composite mechanical properties. An example for a complex three layer woven fabric is demonstrated

Proliferation rates and gene expression profiles in human lymphoblastoid cell lines from patients with depression characterized in response to antidepressant drug therapy

The current therapy success of depressive disorders remains in need of improvement due to low response rates and a delay in symptomatic improvement. Reliable functional biomarkers would be necessary to predict the individual treatment outcome. On the basis of the neurotrophic hypothesis of antidepressant's action, effects of antidepressant drugs on proliferation may serve as tentative individual markers for treatment efficacy. We studied individual differences in antidepressant drug effects on cell proliferation and gene expression in lymphoblastoid cell lines (LCLs) derived from patients treated for depression with documented clinical treatment outcome. Cell proliferation was characterized by EdU (5-ethynyl-2'-deoxyuridine) incorporation assays following a 3-week incubation with therapeutic concentrations of fluoxetine. Genome-wide expression profiling was conducted by microarrays, and candidate genes such as betacellulin—a gene involved in neuronal stem cell regeneration—were validated by quantitative real-time PCR. Ex vivo assessment of proliferation revealed large differences in fluoxetine-induced proliferation inhibition between donor LCLs, but no association with clinical response was observed. Genome-wide expression analyses followed by pathway and gene ontology analyses identified genes with different expression before vs after 21-day incubation with fluoxetine. Significant correlations between proliferation and gene expression of WNT2B, FZD7, TCF7L2, SULT4A1 and ABCB1 (all involved in neurogenesis or brain protection) were also found. Basal gene expression of SULT4A1 (P=0.029), and gene expression fold changes of WNT2B by ex vivo fluoxetine (P=0.025) correlated with clinical response and clinical remission, respectively. Thus, we identified potential gene expression biomarkers eventually being useful as baseline predictors or as longitudinal targets in antidepressant therapy

CHL1, ITGB3 and SLC6A4 gene expression and antidepressant drug response: results from the Munich Antidepressant Response Signature (MARS) study

Aim: The identification of antidepressant drugs (ADs) response biomarkers in depression is of high clinical importance. We explored CHL1 and ITGB3 expression as tentative response biomarkers. Materials & methods: In vitro sensitivity to ADs, as well as gene expression and genetic variants of the candidate genes CHL1, ITGB3 and SLC6A4 were measured in lymphoblastoid cell lines (LCLs) of 58 depressed patients. Results: An association between the clinical remission of depression and the basal expression of CHL1 and ITGB3 was discovered. Individuals whose LCLs expressed higher levels of CHL1 or ITGB3 showed a significantly better remission upon AD treatment. In addition individuals with the CHL1 rs1516338 TT genotype showed a significantly better remission after 5 weeks AD treatment than those carrying a CC genotype. No association between the in vitro sensitivity of LCLs toward AD and the clinical remission could be detected. Conclusion: CHL1 expression in patient-derived LCLs correlated with the clinical outcome. Thus, it could be a valid biomarker to predict the success of an antidepressant therapy

New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR*D studies: rare variant analysis and high-density imputation

Genome-wide association studies have generally failed to identify polymorphisms associated with antidepressant response. Possible reasons include limited coverage of genetic variants that this study tried to address by exome genotyping and dense imputation. A meta-analysis of Genome-Based Therapeutic Drugs for Depression (GENDEP) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D) studies was performed at the single-nucleotide polymorphism (SNP), gene and pathway levels. Coverage of genetic variants was increased compared with previous studies by adding exome genotypes to previously available genome-wide data and using the Haplotype Reference Consortium panel for imputation. Standard quality control was applied. Phenotypes were symptom improvement and remission after 12 weeks of antidepressant treatment. Significant findings were investigated in NEWMEDS consortium samples and Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) for replication. A total of 7062 950 SNPs were analyzed in GENDEP (n=738) and STAR*D (n=1409). rs116692768 (P=1.80e-08, ITGA9 (integrin α9)) and rs76191705 (P=2.59e-08, NRXN3 (neurexin 3)) were significantly associated with symptom improvement during citalopram/escitalopram treatment. At the gene level, no consistent effect was found. At the pathway level, the Gene Ontology (GO) terms GO: 0005694 (chromosome) and GO: 0044427 (chromosomal part) were associated with improvement (corrected P=0.007 and 0.045, respectively). The association between rs116692768 and symptom improvement was replicated in PGRN-AMPS (P=0.047), whereas rs76191705 was not. The two SNPs did not replicate in NEWMEDS. ITGA9 codes for a membrane receptor for neurotrophins and NRXN3 is a transmembrane neuronal adhesion receptor involved in synaptic differentiation. Despite their meaningful biological rationale for being involved in antidepressant effect, replication was partial. Further studies may help in clarifying their role