Electrohydrodynamic jet printing of PZT thick film micro-scale structures

This paper reports the use of a printing technique, called electrohydrodynamic jet printing, for producing PZT thick film micro-scale structures without additional material removing processes. The PZT powder was ball-milled and the effect of milling time on the particle size was examined. This ball-milling process can significantly reduce the PZT particle size and help to prepare stable composite slurry suitable for the E-Jet printing. The PZT micro-scale structures with different features were produced. The PZT lines with different widths and separations were fabricated through the control of the E-Jet printing parameters. The widths of the PZT lines were varied from 80 μm to 200 μm and the separations were changed from 5 μm to 200 μm. In addition, PZT walled structures were obtained by multi-layer E-Jet printing. The E-Jet printed PZT thick films exhibited a relative permittivity (ɛr) of ∼233 and a piezoelectric constant (d33, f) of ∼66 pC N−1

The Impact of Sleep Debt on Excess Adiposity and Insulin Sensitivity in Patients with Early Type 2 Diabetes Mellitus

STUDY OBJECTIVES: We examined cross-sectional and prospective associations between sleep debt and adiposity measures, as well as homeostatic model assessment-insulin resistance (HOMA-IR) in early type 2 diabetes. METHODS: Prospective data analysis from participants of a randomized controlled trial based on an intensive lifestyle intervention (usual care, diet, or diet and physical activity). Data were collected at baseline, 6 months, and 12 months post-intervention. The study was performed across five secondary care centers in the United Kingdom. Patients (n = 593) with a recent diagnosis of type 2 diabetes were recruited. Objective height and weight were ascertained for obesity status (body mass index [BMI]; ≥ 30 kg/m(2)), waist circumference (cm) for central adiposity, and fasting blood samples drawn to examine insulin resistance (IR). Seven-day sleep diaries were used to calculate weekday sleep debt at baseline, calculated as average weekend sleep duration minus average weekday sleep duration. RESULTS: At baseline, compared to those without weekday sleep debt, those with weekday sleep debt were 72% more likely to be obese (OR = 1.72 [95% CI:1.03–2.88]). At six months, weekday sleep debt was significantly associated with obesity and IR after adjustment, OR = 1.90 (95% CI:1.10–3.30), OR = 2.07 (95% CI:1.02–4.22), respectively. A further increase at 12 months was observed for sleep debt with obesity and IR: OR = 2.10 (95% CI:1.14–3.87), OR = 3.16 (95% CI:1.38–7.24), respectively. For every 30 minutes of weekday sleep debt, the risk of obesity and IR at 12 months increased by 18% and 41%, respectively. CONCLUSIONS: Sleep debt resulted in long-term metabolic disruption, which may promote the progression of type 2 diabetes in newly diagnosed patients. Sleep hygiene/education could be an important factor for future interventions to target early diabetes. CITATION: Arora T, Chen MZ, Cooper AR, Andrews RC, Taheri S. The impact of sleep debt on excess adiposity and insulin sensitivity in patients with early type 2 diabetes mellitus. J Clin Sleep Med 2016;12(5):673–680

Explicit kinetic heterogeneity: mechanistic models for interpretation of labeling data of heterogeneous cell populations

Estimation of division and death rates of lymphocytes in different conditions is vital for quantitative understanding of the immune system. Deuterium, in the form of deuterated glucose or heavy water, can be used to measure rates of proliferation and death of lymphocytes in vivo. Inferring these rates from labeling and delabeling curves has been subject to considerable debate with different groups suggesting different mathematical models for that purpose. We show that the three models that are most commonly used are in fact mathematically identical and differ only in their interpretation of the estimated parameters. By extending these previous models, we here propose a more mechanistic approach for the analysis of data from deuterium labeling experiments. We construct a model of "kinetic heterogeneity" in which the total cell population consists of many sub-populations with different rates of cell turnover. In this model, for a given distribution of the rates of turnover, the predicted fraction of labeled DNA accumulated and lost can be calculated. Our model reproduces several previously made experimental observations, such as a negative correlation between the length of the labeling period and the rate at which labeled DNA is lost after label cessation. We demonstrate the reliability of the new explicit kinetic heterogeneity model by applying it to artificially generated datasets, and illustrate its usefulness by fitting experimental data. In contrast to previous models, the explicit kinetic heterogeneity model 1) provides a mechanistic way of interpreting labeling data; 2) allows for a non-exponential loss of labeled cells during delabeling, and 3) can be used to describe data with variable labeling length

Entropy and information in neural spike trains: Progress on the sampling problem

The major problem in information theoretic analysis of neural responses and other biological data is the reliable estimation of entropy--like quantities from small samples. We apply a recently introduced Bayesian entropy estimator to synthetic data inspired by experiments, and to real experimental spike trains. The estimator performs admirably even very deep in the undersampled regime, where other techniques fail. This opens new possibilities for the information theoretic analysis of experiments, and may be of general interest as an example of learning from limited data.Comment: 7 pages, 4 figures; referee suggested changes, accepted versio

Universal Statistical Behavior of Neural Spike Trains

We construct a model that predicts the statistical properties of spike trains generated by a sensory neuron. The model describes the combined effects of the neuron's intrinsic properties, the noise in the surrounding, and the external driving stimulus. We show that the spike trains exhibit universal statistical behavior over short times, modulated by a strongly stimulus-dependent behavior over long times. These predictions are confirmed in experiments on H1, a motion-sensitive neuron in the fly visual system.Comment: 7 pages, 4 figure

An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea

Reference phylogenies are crucial for providing a taxonomic framework for interpretation of marker gene and metagenomic surveys, which continue to reveal novel species at a remarkable rate. Greengenes is a dedicated full-length 16S rRNA gene database that provides users with a curated taxonomy based on de novo tree inference. We developed a ‘taxonomy to tree' approach for transferring group names from an existing taxonomy to a tree topology, and used it to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences. We also incorporated explicit rank information provided by the NCBI taxonomy to group names (by prefixing rank designations) for better user orientation and classification consistency. The resulting merged taxonomy improved the classification of 75% of the sequences by one or more ranks relative to the original NCBI taxonomy with the most pronounced improvements occurring in under-classified environmental sequences. We also assessed candidate phyla (divisions) currently defined by NCBI and present recommendations for consolidation of 34 redundantly named groups. All intermediate results from the pipeline, which includes tree inference, jackknifing and transfer of a donor taxonomy to a recipient tree (tax2tree) are available for download. The improved Greengenes taxonomy should provide important infrastructure for a wide range of megasequencing projects studying ecosystems on scales ranging from our own bodies (the Human Microbiome Project) to the entire planet (the Earth Microbiome Project). The implementation of the software can be obtained from http://sourceforge.net/projects/tax2tree/

Cardiac Biomarkers and Risk of Atrial Fibrillation in Chronic Kidney Disease: The CRIC Study.

Background We tested associations of cardiac biomarkers of myocardial stretch, injury, inflammation, and fibrosis with the risk of incident atrial fibrillation (AF) in a prospective study of chronic kidney disease patients. Methods and Results The study sample was 3053 participants with chronic kidney disease in the multicenter CRIC (Chronic Renal Insufficiency Cohort) study who were not identified as having AF at baseline. Cardiac biomarkers, measured at baseline, were NT-proBNP (N-terminal pro-B-type natriuretic peptide), high-sensitivity troponin T, galectin-3, growth differentiation factor-15, and soluble ST-2. Incident AF ("AF event") was defined as a hospitalization for AF. During a median follow-up of 8 years, 279 (9%) participants developed a new AF event. In adjusted models, higher baseline log-transformed NT-proBNP (N-terminal pro-B-type natriuretic peptide) was associated with incident AF (adjusted hazard ratio [HR] per SD higher concentration: 2.11; 95% CI, 1.75, 2.55), as was log-high-sensitivity troponin T (HR 1.42; 95% CI, 1.20, 1.68). These associations showed a dose-response relationship in categorical analyses. Although log-soluble ST-2 was associated with AF risk in continuous models (HR per SD higher concentration 1.35; 95% CI, 1.16, 1.58), this association was not consistent in categorical analyses. Log-galectin-3 (HR 1.05; 95% CI, 0.91, 1.22) and log-growth differentiation factor-15 (HR 1.16; 95% CI, 0.96, 1.40) were not significantly associated with incident AF. Conclusions We found strong associations between higher NT-proBNP (N-terminal pro-B-type natriuretic peptide) and high-sensitivity troponin T concentrations, and the risk of incident AF in a large cohort of participants with chronic kidney disease. Increased atrial myocardial stretch and myocardial cell injury may be implicated in the high burden of AF in patients with chronic kidney disease

Sweet Chestnut (Castanea Sativa Mill.) in Britain: Re-assessment of its Status as a Roman Archaeophyte

The Roman period sees the introduction of many new plants and animals into Britain, with a profound impact on people’s experience of their environment. Sweet chestnut is considered to be one such introduction, for which records of sweet chestnut wood and charcoal from archaeological excavations of Romano-British period contexts have been used as evidence. This paper reviews the records for sweet chestnut in Britain pre-A.D. 650, by critically evaluating original excavation reports and examining archived specimens. This review re-assesses the original identifications of sweet chestnut and/or their dating and concludes that most of the evidence that justified sweet chestnut’s status as a Roman archaeophyte is untenable. The review emphasises the importance of securely identifying and directly dating plant material and of long-term curation by museums and archives

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing