Intergenerational Metabolomic Analysis of Mothers with a History of Gestational Diabetes Mellitus and Their Offspring

Shared metabolomic patterns at delivery have been suggested to underlie the mother-to-child transmission of adverse metabolic health. This study aimed to investigate whether mothers with gestational diabetes mellitus (GDM) and their offspring show similar metabolomic patterns several years postpartum. Targeted metabolomics (including 137 metabolites) was performed in plasma samples obtained during an oral glucose tolerance test from 48 mothers with GDM and their offspring at a cross-sectional study visit 8 years after delivery. Partial Pearson’s correlations between the area under the curve (AUC) of maternal and offspring metabolites were calculated, yielding so-called Gaussian graphical models. Spearman’s correlations were applied to investigate correlations of body mass index (BMI), Matsuda insulin sensitivity index (ISI-M), dietary intake, and physical activity between generations, and correlations of metabolite AUCs with lifestyle variables. This study revealed that BMI, ISI-M, and the AUC of six metabolites (carnitine, taurine, proline, SM(-OH) C14:1, creatinine, and PC ae C34:3) were significantly correlated between mothers and offspring several years postpartum. Intergenerational metabolite correlations were independent of shared BMI, ISI-M, age, sex, and all other metabolites. Furthermore, creatinine was correlated with physical activity in mothers. This study suggests that there is long-term metabolic programming in the offspring of mothers with GDM and informs us about targets that could be addressed by future intervention studies.     

Supramolecular Assembly of Aminoethylene‐Lipopeptide PMO Conjugates into RNA Splice‐Switching Nanomicelles

Phosphorodiamidate morpholino oligomers (PMOs) are oligonucleotide analogs that can be used for therapeutic modulation of pre‐mRNA splicing. Similar to other classes of nucleic acid‐based therapeutics, PMOs require delivery systems for efficient transport to the intracellular target sites. Here, artificial peptides based on the oligo(ethylenamino) acid succinyl‐tetraethylenpentamine (Stp), hydrophobic modifications, and an azide group are presented, which are used for strain‐promoted azide‐alkyne cycloaddition conjugation with splice‐switching PMOs. By systematically varying the lead structure and formulation, it is determined that the type of contained fatty acid and supramolecular assembly have a critical impact on the delivery efficacy. A compound containing linolenic acid with three cis double bonds exhibits the highest splice‐switching activity and significantly increases functional protein expression in pLuc/705 reporter cells in vitro and after local administration in vivo. Structural and mechanistic studies reveal that the lipopeptide PMO conjugates form nanoparticles, which accelerate cellular uptake and that the content of unsaturated fatty acids enhances endosomal escape. In an in vitro Duchenne muscular dystrophy exon skipping model using H2K‐mdx52 dystrophic skeletal myotubes, the highly potent PMO conjugates mediate significant splice‐switching at very low nanomolar concentrations. The presented aminoethylene‐lipopeptides are thus a promising platform for the generation of PMO‐therapeutics with a favorable activity/toxicity profile.     

Microscopic simulation of electronic noise in semiconductormaterials and devices

We present a microscopic interpretation of electronic noise in semiconductor materials and two-terminal devices. The theory is based on Monte Carlo simulations of the carrier motion self-consistently coupled with a Poisson solver. Current and voltage noise operations are applied and their respective representations discussed. As application we consider the cases of homogeneous materials, resistors, n⁺nn ⁺ structures, and Schottky-barrier diodes. Phenomena associated with coupling between fluctuations in carrier velocity and self-consistent electric field are quantitatively investigated for the first time. At increasing applied fields hot-carrier effects are found to be of relevant importance in all the cases considered here. As a general result, noise spectroscopy is found to be a source of valuable information to investigate and characterize transport properties of semiconductor materials and devices     

Elevated temperature performance of pseudomorphic AlGaAs/InGaAsMODFETs

Parametric DC measurements on pseudomorphic AlGaAs/InGaAs modulation-doped field-effect transistors (MODFETs) were carried out over the 300-405 K temperature range. A gradual channel device model was developed to simulate the temperature dependent behavior and assist in the interpretation of the characteristics. The simulations are shown to provide good predictive ability and confirm the physical reasons why the zero temperature coefficient point of a MODFET occurs only for gate bias voltages below the threshold voltage     

Effects of category prototypes on performance-rating accuracy.

Two studies examined whether the accessibility of performance prototypes influences performance appraisals. Pilot studies revealed students used performance prototypes when rating instructor performance. Study 1 manipulated the accessibility of these prototypes and the time delay of performance ratings. Results showed no effect of the prime on rating error and accuracy; however, discrimination accuracy decreased over time and recognition bias became more conservative. Study 2 manipulated prototype accessibility and type of rating stimuli (videotape vs. vignette). Rating accuracy and recall were higher for vignette than videotape stimuli, and only those participants exposed to the vignette exhibited priming effects. Results supported transfer-appropriate processing and implied that cognitive primes may have a stronger effect on performance ratings based on "paper-people" than videotaped stimuli. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The application of semantic classification trees to naturallanguage understanding

This article describes a new method for building a natural language understanding (NLU) system, in which the system's rules are learnt automatically from training data. The method has been applied to design of a speech understanding (SU) system. Designers of such systems rely increasingly on robust matchers to perform the task of extracting meaning from one or several word sequence hypotheses generated by a speech recognizer. We describe a new data structure, the semantic classification tree (SCT), that learns semantic rules from training data and can be a building block for robust matchers for NLU tasks. By reducing the need for handcoding and debugging a large number of rules, this approach facilitates rapid construction of an NLU system. In the case of an SU system, the rules learned by an SCT are highly resistant to errors by the speaker or by the speech recognizer because they depend on a small number of words in each utterance. Our work shows that semantic rules can be learned automatically from training data, yielding successful NLU for a realistic application     

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Expanding the MOS: Development and Psychometric Evaluation of the MOS-R and MOS-X

The Mean Opinion Scale (MOS) is a questionnaire used to obtain listeners' subjective assessments of synthetic speech. This paper documents the motivation, method, and results of six experiments conducted from 1999 to 2002 that investigated the psychometric properties of the MOS and expanded the range of speech characteristics it evaluates. Our initial experiments documented the reliability, validity, sensitivity, and factor structure of the P.L. Salza et al. (Acta Acustica, Vol. 82, pp. 650–656, 1996) MOS and used psychometric principles to revise and improve the scale. This work resulted in the MOS-Revised (MOS-R). Four subsequent experiments expanded the MOS-R beyond its previous focus on Intelligibility and Naturalness, to include measurement of the Prosody and Social Impression of synthetic voices. As a result of this work, we created the MOS-Expanded (MOS-X), a rating scale shown to be reliable, valid, and sensitive for high-quality evaluation of synthetic speech in applied industrial settings.     

Ultrastructure of a bio-electrolytic methanogenic/methanotrophic granular biofilm for the complete degradation of tetrachloroethylene in contaminated groundwater.

The electrolytical methanogenic/methanotrophic coupling (eMaMoC) process was tested in a laboratory-scale single-stage reactor for the treatment of tetrachloroethene (PCE)-contaminated waters. A water electrolysis cell was placed directly in the effluent recirculation loop for the supply of both O2 and H2 to the system: H2 serving as the electron donor for both carbonate reduction into CH4 and reductive dechlorination. The concurrent presence of O2 and CH4 could be used by the methanotrophs for co-metabolically oxidising the chlorinated intermediates left over by the anaerobic transformation of PCE. At a PCE inlet of 33-52 microM and a hydraulic residence time (HRT) of 5.6 days, PCE reductive dechlorination to dichloroethene (DCE) was over 95% with a maximum DCE mineralisation of 83%. Fluorescence in situ hybridisation with 16S rRNA probes related to type I and type II methanotrophic bacteria were utilised to localise the methanotrophic communities in the anaerobic/aerobic granules. It evidenced that with operational time, along with increasing oxygenation rate, methanotrophic communities were specifically colonising onto the outermost layer of the anaerobic/aerobic granule.