P2X1 and P2X5 subunits form the functional P2X receptor in mouse cortical astrocytes

ATP plays an important role in signal transduction between neuronal and glial circuits and within glial networks. Here we describe currents activated by ATP in astrocytes acutely isolated from cortical brain slices by non-enzymatic mechanical dissociation. Brain slices were prepared from transgenic mice that express enhanced green fluorescent protein under the control of the human glial fibrillary acidic protein promoter. Astrocytes were studied by whole-cell voltage clamp. Exogenous ATP evoked inward currents in 75 of 81 astrocytes. In the majority (~65%) of cells, ATP-induced responses comprising a fast and delayed component; in the remaining subpopulation of astrocytes, ATP triggered a smoother response with rapid peak and slowly decaying plateau phase. The fast component of the response was sensitive to low concentrations of ATP (with EC50 of ~40 nM). All ATP-induced currents were blocked by pyridoxal-phosphate-6-azophenyl-2',4'-disulfonate (PPADS); they were insensitive to ivermectin. Quantitative real-time PCR demonstrated strong expression of P2X1 and P2X5 receptor subunits and some expression of P2X2 subunit mRNAs. The main properties of the ATP-induced response in cortical astrocytes (high sensitivity to ATP, biphasic kinetics, and sensitivity to PPADS) were very similar to those reported for P2X1/5 heteromeric receptors studied previously in heterologous expression systems

Personalized smart environments to increase inclusion of people with Down's Syndrome

Most people with Downs Syndrome (DS) experience low integration with society. Recent research and new opportunities for their integration in mainstream education and work provided numerous cases where levels of achievement exceeded the (limiting) expectations. This paper describes a project, POSEIDON, aiming at developing a technological infrastructure which can foster a growing number of services developed to support people with DS. People with DS have their own strengths, preferences and needs so POSEIDON will focus on using their strengths to provide support for their needs whilst allowing each individual to personalize the solution based on their preferences. This project is user-centred from its inception and will give all main stakeholders ample opportunities to shape the output of the project, which will ensure a final outcome which is of practical usefulness and interest to the intended users

A statistical approach to optimizing paper spray mass spectrometry parameters

Rationale Paper spray mass spectrometry (PS‐MS) was used to analyze and quantify ampicillin, a hydrophilic compound and frequently utilized antibiotic. Hydrophilic molecules are difficult to analyze via PS‐MS due to their strong binding affinity to paper substrates and low ionization efficiency, among other reasons. Methods Solvent and paper parameters were optimized to increase the extraction of ampicillin from the paper substrate. After optimizing these key parameters, a Resolution IV 1/16 fractional factorial design with two center points was employed to screen eight different design parameters simultaneously. Results Pore size, sample volume, and solvent volume were the most significant factors affecting average peak area under the curve (AUC) and the signal‐to‐blank (S/B) ratio for the 1 μg/mL ampicillin calibrant. After optimizing the key parameters, a linear calibration curve with a range of 0.2 μg/mL to 100 μg/mL was generated (R2 = 0.98) and the limit of detection (LOD) and lower limit of quantification (LLOQ) were calculated to be 0.07 μg/mL and 0.25 μg/mL, respectively. Conclusions The statistical optimization procedure undertaken here increased the mass spectral signal intensity by more than a factor of 40. This statistical method of screening followed by optimization experiments proved faster and more efficient, and produced more drastic improvements than typical one‐factor‐at‐a‐time experiments

The 1991 Field Evaluation of Herbicides on Small Fruit, Vegetables and Ornamental Crops

The establishment of this field-testing procedure provides the chemical industry, through its partial support, and the Arkansas Experiment Station the opportunity to evaluate herbicide performance on small fruit, vegetable and ornamental crops grown under Arkansas conditions. This report also provides a means for disseminating information to interested people and public-service weed scientists

Listen to genes : dealing with microarray data in the frequency domain

Background: We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes. Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000 genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail. Conclusions: We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of potential interest to Arabidopsis researchers

Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait

Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system

Monitoring of lung edema by microwave reflectometry during lung ischemia-reperfusion injury in vivo

It is still unclear whether lung edema can be monitored by microwave reflectometry and whether the measured changes in lung dry matter content (DMC) are accompanied by changes in PaO(2) and in pro-to anti-inflammatory cytokine expression (IFN-gamma and IL-10). Right rat lung hili were cross-clamped at 37 degrees C for 0, 60, 90 or 120 min ischemia followed by 120 min reperfusion. After 90 min (DMC: 15.9 +/- 1.4%; PaO(2): 76.7 +/- 18 mm Hg) and 120 min ischemia (DMC: 12.8 +/- 0.6%; PaO(2): 43 +/- 7 mm Hg), a significant decrease in DMC and PaO(2) throughout reperfusion compared to 0 min ischemia (DMC: 19.5 +/- 1.11%; PaO(2): 247 +/- 33 mm Hg; p < 0.05) was observed. DMC and PaO(2) decreased after 60 min ischemia but recovered during reperfusion (DMC: 18.5 +/- 2.4%; PaO(2) : 173 +/- 30 mm Hg). DMC values reflected changes on the physiological and molecular level. In conclusion, lung edema monitoring by microwave reflectometry might become a tool for the thoracic surgeon. Copyright (c) 2006 S. Karger AG, Basel

Analytical Bethe Ansatz for closed and open gl(n)-spin chains in any representation

We present an "algebraic treatment" of the analytical Bethe Ansatz. For this purpose, we introduce abstract monodromy and transfer matrices which provide an algebraic framework for the analytical Bethe Ansatz. It allows us to deal with a generic gl(n)-spin chain possessing on each site an arbitrary gl(n)-representation. For open spin chains, we use the classification of the reflection matrices to treat all the diagonal boundary cases. As a result, we obtain the Bethe equations in their full generality for closed and open spin chains. The classifications of finite dimensional irreducible representations for the Yangian (closed spin chains) and for the reflection algebras (open spin chains) are directly linked to the calculation of the transfer matrix eigenvalues. As examples, we recover the usual closed and open spin chains, we treat the alternating spin chains and the closed spin chain with impurity

Antidepressant effects of a single dose of ayahuasca in patients with recurrent depression: a preliminary report

Objectives: Ayahuasca (AYA), a natural psychedelic brew prepared from Amazonian plants and rich in dimethyltryptamine (DMT) and harmine, causes effects of subjective well-being and may therefore have antidepressant actions. This study sought to evaluate the effects of a single dose of AYA in six volunteers with a current depressive episode. Methods: Open-label trial conducted in an inpatient psychiatric unit. Results: Statistically significant reductions of up to 82% in depressive scores were observed between baseline and 1, 7, and 21 days after AYA administration, as measured on the Hamilton Rating Scale for Depression (HAM-D), the Montgomery-Åsberg Depression Rating Scale (MADRS), and the Anxious-Depression subscale of the Brief Psychiatric Rating Scale (BPRS). AYA administration resulted in nonsignificant changes in Young Mania Rating Scale (YMRS) scores and in the thinking disorder subscale of the BPRS, suggesting that AYA does not induce episodes of mania and/or hypomania in patients with mood disorders and that modifications in thought content, which could indicate psychedelic effects, are not essential for mood improvement. Conclusions: These results suggest that AYA has fast-acting anxiolytic and antidepressant effects in patients with a depressive disorder

Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process

Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning these networks is challenging due to the low sample size and high dimensionality of genomic data. Results: We present a novel and highly efficient approach to estimate a VAR network. This proceeds in two steps: (i) improved estimation of VAR regression coefficients using an analytic shrinkage approach, and (ii) subsequent model selection by testing the associated partial correlations. In simulations this approach outperformed for small sample size all other considered approaches in terms of true discovery rate (number of correctly identified edges relative to the significant edges). Moreover, the analysis of expression time series data from Arabidopsis thaliana resulted in a biologically sensible network. Conclusion: Statistical learning of large-scale VAR causal models can be done efficiently by the proposed procedure, even in the difficult data situations prevalent in genomics and proteomics. Availability: The method is implemented in R code that is available from the authors on request