Evaluation of shared genetic susceptibility loci between autoimmune diseases and schizophrenia based on genome-wide association studies.

BACKGROUND: Epidemiological studies have documented higher than expected comorbidity (or, in some cases, inverse comorbidity) between schizophrenia and several autoimmune disorders. It remains unknown whether this comorbidity reflects shared genetic susceptibility loci. AIMS: The present study aimed to investigate whether verified genome wide significant variants of autoimmune disorders confer risk of schizophrenia, which could suggest a common genetic basis. METHODS: Seven hundred and fourteen genome wide significant risk variants of 25 autoimmune disorders were extracted from the NHGRI GWAS catalogue and examined for association to schizophrenia in the Psychiatric Genomics Consortium schizophrenia GWAS samples (36,989 cases and 113,075 controls). RESULTS: Two independent loci at 4q24 and 6p21.32-33 originally identified from GWAS of autoimmune diseases were found genome wide associated with schizophrenia (1.7 × 10(-8 )≥( )p ≥ 4.0 × 10(-21)). While these observations confirm the existence of shared genetic susceptibility loci between schizophrenia and autoimmune diseases, the findings did not show a significant enrichment. CONCLUSION: The findings do not support a genetic overlap in common SNPs between autoimmune diseases and schizophrenia that in part could explain the observed comorbidity from epidemiological studies

Implementation of a Quantum Search Algorithm on a Nuclear Magnetic Resonance Quantum Computer

We demonstrate an implementation of a quantum search algorithm on a two qubit NMR quantum computer based on cytosine.Comment: Six pages, three figure

A rule-based approach to implicit emotion detection in text

Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17–30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on “Happy”, “Angry-Disgust” and “Sad”, even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text

OH Production from the Photolysis of Isoprene-derived Peroxy Radicals: Cross-sections, quantum yields and atmospheric implications

In environments with high concentrations of biogenic volatile organic compounds and low concentrations of nitrogen oxides (NOx = NO + NO2), significant discrepancies have been found between measured and modeled concentrations of hydroxyl radical (OH). The photolysis of peroxy radicals from isoprene (HO-Iso-O2) in the near ultraviolet represents a potential source of OH in these environments, yet has not been considered in atmospheric models. This paper presents measurements of the absorption cross-sections for OH formation (σRO2,OH) from the photolysis of HO-Iso-O2 at wavelengths from 310–362.5 nm via direct observation by laser-induced fluorescence of the additional OH produced following laser photolysis of HO-Iso-O2. Values of σRO2,OH for HO-Iso-O2 ranged from (6.0 ± 1.6) × 10-20 cm2 molecule-1 at 310 nm to (0.5 ± 0.15) × 10-20 cm2 molecule-1 at 362.5 nm. OH photodissociation yields from HO-Iso-O2 photolysis, ϕOH,RO2, were determined via comparison of the measured values of σRO2,OH to the total absorption cross-sections for HO-Iso-O2 (σRO2), which were obtained using a newly-constructed spectrometer. ϕOH,RO2 was determined to be 0.13 ± 0.037 at wavelengths from 310–362.5 nm. To determine the impact of HO-Iso-O2 photolysis on atmospheric OH concentrations, a modeling case-study for a high-isoprene, low-NOx environment (namely, the 2008 Oxidant and Particle Photochemical Processes above a South-East Asian Tropical Rainforest (OP-3) field campaign, conducted in Borneo) was undertaken using the detailed Master Chemical Mechanism. The model calculated that the inclusion of HO-Iso-O2 photolysis in the model had increased the OH concentration by only 1% on average from 10:00–16:00 local time. Thus, HO-Iso-O2 photolysis alone is insufficient to resolve the discrepancy seen between measured OH concentrations and those predicted by atmospheric chemistry models in such environments

Unmixing oscillatory brain activity by EEG source localization and empirical mode decomposition

Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin

On the 'centre of gravity' method for measuring the composition of magnetite/maghemite mixtures, or the stoichiometry of magnetite-maghemite solid solutions, via Fe-57 Mossbauer spectroscopy

We evaluate the application of 57Fe Mössbauer spectroscopy to the determination of the composition of magnetite (Fe3O4)/maghemite (γ-Fe2O3) mixtures and the stoichiometry of magnetite-maghemite solid solutions. In particular, we consider a recently proposed model-independent method which does not rely on a priori assumptions regarding the nature of the sample, other than that it is free of other Fe-containing phases. In it a single parameter, δRT—the ‘centre of gravity’, or area weighted mean isomer shift at room temperature, T = 295 ± 5 K—is extracted by curve-fitting a sample’s Mössbauer spectrum, and is correlated to the sample’s composition or stoichiometry. We present data on highpurity magnetite and maghemite powders, and mixtures thereof, as well as comparison literature data from nanoparticulate mixtures and solid solutions, to show that a linear correlation exists between δRT and the numerical proportion of Fe atoms in the magnetite environment: α = Femagnetite/Fetotal = − ( ) δ δ RT o /m, where δo = 0.3206 ± 0.0022mm s−1 and m = 0.2135 ± 0.0076mm s−1 . We also present equations to relate α to the weight percentage w of magnetite in mixed phases, and the magnetite stoichiometry x = Fe2+/Fe3+ in solid solutions. The analytical method is generally applicable, but is most accurate when the absorption profiles are sharp; in some samples this may require spectra to be recorded at reduced temperatures. We consider such cases and provide equations to relate δ ( ) T to the corresponding α value

Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance

Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC

Published and not fully published double-blind, randomised, controlled trials with oral naratriptan in the treatment of migraine: a review based on the GSK Trial Register

Naratriptan 2.5 mg is now an over-the-counter drug in Germany. This should increase the interest in drug. The GSK Trial Register was searched for published and unpublished double-blind, randomised, controlled trials (RCTs) concerning the use of naratriptan in migraine. Only 7 of 17 RCTs are published in full. Naratriptan 2.5 mg is superior to placebo for acute migraine treatment in 6 RCTs, but inferior to sumatriptan 100 mg and rizatriptan 10 mg in one RCT each. This dose of naratriptan has no more adverse events than placebo. Naratriptan 1 mg b.i.d. has some effect in the short-term prophylactic treatment of menstruation-associated migraine in 3 RCTs. In 2 RCTs, naratriptan 2.5 mg was equivalent to naproxen sodium 375 mg for migraine-related quality of life. Naratriptan 2.5 mg (34% preference) was superior to naproxen sodium 500 mg (25% preference). Naratriptan 2.5 mg is better than placebo in the acute treatment of migraine. The adverse effect profile of naratriptan 2.5 mg is similar to that of placebo. The efficacy of naratriptan 2.5 mg versus NSAIDs is not sufficiently investigated. Naratriptan, when available OTC is a reasonable second or third choice on the step care ladder in the acute treatment of migraine

Diagnosis and management of subcutaneous implantable cardioverter‐defibrillator infections based on process mapping

Background: Infection is a well‐recognized complication of cardiovascular implantable electronic device (CIED) implantation, including the more recently available subcutaneous implantable cardioverter‐defibrillator (S‐ICD). Although the AHA/ACC/HRS guidelines include recommendations for S‐ICD use, currently there are no clinical trial data that address the diagnosis and management of S‐ICD infections. Therefore, an expert panel was convened to develop consensus on these topics. / Methods: A process mapping methodology was used to achieve a primary goal – the development of consensus on the diagnosis and management of S‐ICD infections. Two face‐to‐face meetings of panel experts were conducted to recommend useful information to clinicians in individual patient management of S‐ICD infections. / Results: Panel consensus of a stepwise approach in the diagnosis and management was developed to provide guidance in individual patient management. / Conclusion: Achieving expert panel consensus by process mapping methodology in S‐ICD infection diagnosis and management was attainable, and the results should be helpful in individual patient management