Withdrawing intra-aortic balloon pump support paradoxically improves microvascular flow

Introduction: The Intra-Aortic Balloon Pump (IABP) is frequently used to mechanically support the heart. There is evidence that IABP improves microvascular flow during cardiogenic shock but its influence on the human microcirculation in patients deemed ready for discontinuing IABP support has not yet been studied. Therefore we used sidestream dark field imaging (SDF) to test our hypothesis that human microcirculation remains unaltered with or without IABP support in patients clinically ready for discontinuation of mechanical support. Methods: We studied 15 ICU patients on IABP therapy. Measurements were performed after the clinical decision was made to remove the balloon catheter. We recorded global hemodynamic parameters and performed venous oximetry during maximal IABP support (1:1) and 10 minutes after temporarily stopping the IABP therapy. At both time points, we also recorded video clips of the sublingual microcirculation. From these we determined indices of microvascular perfusion including perfused vessel density (PVD) and microvascular flow index (MFI). Results: Ceasing IABP support lowered mean arterial pressure (74 +/- 8 to 71 +/- 10 mmHg; P = 0.048) and increased diastolic pressure (43 +/- 10 to 53 +/- 9 mmHg; P = 0.0002). However, at the level of the microcirculation we found an increase of PVD of small vessels <20 mu m (5.47 +/- 1.76 to 6.63 +/- 1.90; P = 0.0039). PVD for vessels >20 mu m and MFI for both small and large vessels were unaltered. During the procedure global oxygenation parameters (ScvO(2)/SvO(2)) remained unchanged. Conclusions: In patients deemed ready for discontinuing IABP support according to current practice, SDF imaging showed an increase of microcirculatory flow of small vessels after ceasing IABP therapy. This observation may indicate that IABP impairs microvascular perfusion in recovered patients, although this warrants confirmatio

Comparison of different pain scoring systems in critically ill patients in a general ICU

Background: Pain in critically ill patients in the intensive care unit (ICU) is common. However, pain assessment in critically ill patients often is complicated because these patients are unable to communicate effectively. Therefore, we designed a study (a) to determine the inter-rater reliability of the Numerical Rating Scale (NRS) and the Behavioral Pain Scale (BPS), (b) to compare pain scores of different observers and the patient, and (c) to compare NRS, BPS, and the Visual Analog Scale (VAS) for measuring pain in patients in the ICU. Methods: We performed a prospective observational study in 113 non-paralyzed critically ill patients. The attending nurses, two researchers, and the patient (when possible) obtained 371 independent observation series of NRS, BPS, and VAS. Data analyses were performed on the sample size of patients (n = 113). Results: Inter-rater reliability of the NRS and BPS proved to be adequate (kappa = 0.71 and 0.67, respectively). The level of agreement within one scale point between NRS rated by the patient and NRS scored by attending nurses was 73%. However, high patient scores (NRS ≥4) were underestimated by nurses (patients 33% versus nurses 18%). In responsive patients, a high correlation between NRS and VAS was found (rs= 0.84, P < 0.001). In ventilated patients, a moderate positive correlation was found between the NRS and the BPS (rs= 0.55, P < 0.001). However, whereas 6% of the observations were NRS of greater than or equal to 4, BPS scores were all very low (median 3.0, range 3.0 to 5.0). Conclusion: The different scales show a high reliability, but observer-based evaluation often underestimates the pain, particularly in the case of high NRS values (≥4) rated by the patient. Therefore, whenever this is possible, ICU patients should rate their pain. In unresponsive patients, primarily the attending nurse involved in daily care should score the patient's pain. In ventilated patients, the BPS should be used only in conjunction with the NRS nurse to measure pain levels in the absence of painful stimuli

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe

The Role of Superior Temporal Cortex in Auditory Timing

Recently, there has been upsurge of interest in the neural mechanisms of time perception. A central question is whether the representation of time is distributed over brain regions as a function of stimulus modality, task and length of the duration used or whether it is centralized in a single specific and supramodal network. The answers seem to be converging on the former, and many areas not primarily considered as temporal processing areas remain to be investigated in the temporal domain. Here we asked whether the superior temporal gyrus, an auditory modality specific area, is involved in processing of auditory timing. Repetitive transcranial magnetic stimulation was applied over left and right superior temporal gyri while participants performed either a temporal or a frequency discrimination task of single tones. A significant decrease in performance accuracy was observed after stimulation of the right superior temporal gyrus, in addition to an increase in response uncertainty as measured by the Just Noticeable Difference. The results are specific to auditory temporal processing and performance on the frequency task was not affected. Our results further support the idea of distributed temporal processing and speak in favor of the existence of modality specific temporal regions in the human brain

PCR clonality detection in Hodgkin lymphoma

B-cell clonality detection in whole tissue is considered indicative of B-cell non-Hodgkin lymphoma (NHL). We tested frozen tissue of 24 classical Hodgkin lymphomas (cHL) with a varying tumor cell load with the multiplex polymerase chain reaction (PCR) primer sets for IGH and IGK gene rearrangement (BIOMED-2). A clonal population was found in 13 cases with the IGH FR1 and/or FR2/FR3 PCRs. Using the IGK-VJ and IGK-DE PCRs, an additional six cases had a dominant clonal cell population, resulting in a detection rate of 79% in frozen tissue. Of 12 cases, also the formalin-fixed and paraffin-embedded (FFPE) tissue was tested. Surprisingly, in eight of the 12 FFPE cases with acceptable DNA quality (allowing PCR amplification of >200 nt fragments), the IGK multiplex PCRs performed better in detecting clonality (six out of eight clonal IGK rearrangements) than the IGH PCRs (four out of nine clonal rearrangements), despite a rather large amplicon size. There was no evidence of B-cell lymphoma during follow-up of 1 to 6 years and no correlation was found between the presence of a clonal result and Epstein–Barr virus in the tumor cells. Our results indicate that the present routine PCR methods are sensitive enough to detect small numbers of malignant cells in cHL. Therefore, the presence of a clonal B-cell population does not differentiate between cHL and NHL

Fast motif recognition via application of statistical thresholds

Background: Improving the accuracy and efficiency of motif recognition is an important computational challenge that has application to detecting transcription factor binding sites in genomic data. Closely related to motif recognition is the Consensus String decision problem that asks, given a parameter d and a set of ℓ-length strings S = {s1,...,sn}, whether there exists a consensus string that has Hamming distance at most d from any string in S. A set of strings S is pairwise bounded if the Hamming distance between any pair of strings in S is at most 2d. It is trivial to determine whether a set is pairwise bounded, and a set cannot have a consensus string unless it is pairwise bounded. We use Consensus String to determine whether or not a pairwise bounded set has a consensus. Unfortunately, Consensus String is NP-complete. The lack of an efficient method to solve the Consensus String problem has caused it to become a computational bottleneck in MCL-WMR, a motif recognition program capable of solving difficult motif recognition problem instances. Results: We focus on the development of a method for solving Consensus String quickly with a small probability of error. We apply this heuristic to develop a new motif recognition program, sMCL-WMR, which has impressive accuracy and efficiency. We demonstrate the performance of sMCL-WMR in detecting weak motifs in large data sets and in real genomic data sets, and compare the performance to other leading motif recognitio

Large-scale identification of microRNA targets in murine Dgcr8-deficient embryonic stem cell lines.

Small RNAs such as microRNAs play important roles in embryonic stem cell maintenance and differentiation. A broad range of microRNAs is expressed in embryonic stem cells while only a fraction of their targets have been identified. We have performed large-scale identification of embryonic stem cell microRNA targets using a murine embryonic stem cell line deficient in the expression of Dgcr8. These cells are heavily depleted for microRNAs, allowing us to reintroduce specific microRNA duplexes and identify refined target sets. We used deep sequencing of small RNAs, mRNA expression profiling and bioinformatics analysis of microRNA seed matches in 3' UTRs to identify target transcripts. Consequently, we have identified a network of microRNAs that converge on the regulation of several important cellular pathways. Additionally, our experiments have revealed a novel candidate for Dgcr8-independent microRNA genesis and highlighted the challenges currently facing miRNA annotation

Dynamical Mean-Field Theory

The dynamical mean-field theory (DMFT) is a widely applicable approximation scheme for the investigation of correlated quantum many-particle systems on a lattice, e.g., electrons in solids and cold atoms in optical lattices. In particular, the combination of the DMFT with conventional methods for the calculation of electronic band structures has led to a powerful numerical approach which allows one to explore the properties of correlated materials. In this introductory article we discuss the foundations of the DMFT, derive the underlying self-consistency equations, and present several applications which have provided important insights into the properties of correlated matter.Comment: Chapter in "Theoretical Methods for Strongly Correlated Systems", edited by A. Avella and F. Mancini, Springer (2011), 31 pages, 5 figure

Overexpression of Arabidopsis FLOWERING LOCUS T (FT) gene improves floral development in cassava (Manihot esculenta, Crantz)

Cassava is a tropical storage-root crop that serves as a worldwide source of staple food for over 800 million people. Flowering is one of the most important breeding challenges in cassava because in most lines flowering is late and non-synchronized, and flower production is sparse. The FLOWERING LOCUS T (FT) gene is pivotal for floral induction in all examined angiosperms. The objective of the current work was to determine the potential roles of the FT signaling system in cassava. The Arabidopsis thaliana FT gene (atFT) was transformed into the cassava cultivar 60444 through Agrobacterium-mediated transformation and was found to be overexpressed constitutively. FT overexpression hastened flower initiation and associated fork-type branching, indicating that cassava has the necessary signaling factors to interact with and respond to the atFT gene product. In addition, overexpression stimulated lateral branching, increased the prolificacy of flower production and extended the longevity of flower development. While FT homologs in some plant species stimulate development of vegetative storage organs, atFT inhibited storage-root development and decreased root harvest index in cassava. These findings collectively contribute to our understanding of flower development in cassava and have the potential for applications in breeding

The Hubbard model within the equations of motion approach

The Hubbard model has a special role in Condensed Matter Theory as it is considered as the simplest Hamiltonian model one can write in order to describe anomalous physical properties of some class of real materials. Unfortunately, this model is not exactly solved except for some limits and therefore one should resort to analytical methods, like the Equations of Motion Approach, or to numerical techniques in order to attain a description of its relevant features in the whole range of physical parameters (interaction, filling and temperature). In this manuscript, the Composite Operator Method, which exploits the above mentioned analytical technique, is presented and systematically applied in order to get information about the behavior of all relevant properties of the model (local, thermodynamic, single- and two- particle ones) in comparison with many other analytical techniques, the above cited known limits and numerical simulations. Within this approach, the Hubbard model is shown to be also capable to describe some anomalous behaviors of the cuprate superconductors.Comment: 232 pages, more than 300 figures, more than 500 reference