GABAA receptors can initiate the formation of functional inhibitory GABAergic synapses.

The mechanisms that underlie the selection of an inhibitory GABAergic axon's postsynaptic targets and the formation of the first contacts are currently unknown. To determine whether expression of GABAA receptors (GABAA Rs) themselves - the essential functional postsynaptic components of GABAergic synapses - can be sufficient to initiate formation of synaptic contacts, a novel co-culture system was devised. In this system, the presynaptic GABAergic axons originated from embryonic rat basal ganglia medium spiny neurones, whereas their most prevalent postsynaptic targets, i.e. α1/β2/γ2-GABAA Rs, were expressed constitutively in a stably transfected human embryonic kidney 293 (HEK293) cell line. The first synapse-like contacts in these co-cultures were detected by colocalization of presynaptic and postsynaptic markers within 2 h. The number of contacts reached a plateau at 24 h. These contacts were stable, as assessed by live cell imaging; they were active, as determined by uptake of a fluorescently labelled synaptotagmin vesicle-luminal domain-specific antibody; and they supported spontaneous and action potential-driven postsynaptic GABAergic currents. Ultrastructural analysis confirmed the presence of characteristics typical of active synapses. Synapse formation was not observed with control or N-methyl-d-aspartate receptor-expressing HEK293 cells. A prominent increase in synapse formation and strength was observed when neuroligin-2 was co-expressed with GABAA Rs, suggesting a cooperative relationship between these proteins. Thus, in addition to fulfilling an essential functional role, postsynaptic GABAA Rs can promote the adhesion of inhibitory axons and the development of functional synapses

A Revised Design for Microarray Experiments to Account for Experimental Noise and Uncertainty of Probe Response

Background Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance. Results Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements. Conclusion The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations

A class of efficient high-order iterative methods with memory for nonlinear equations and their dynamics

[EN] In this paper we obtain some theoretical results about iterative methods with memory for nonlinear equations. The class of algorithms we consider focus on incorporating memory without increasing the computational cost of the algorithm. This class uses for the predictor step of each iteration a quantity that has already been calculated in the previous iteration, typically the quantity governing the slope from the previous corrector step. In this way we do not introduce any extra computation, and more importantly, we avoid new function evaluations, allowing us to obtain high-order iterative methods in a simple way. A specific class of methods of this type is introduced, and we prove the convergence order is 2(n) + 2(n-2) with n + 1 function evaluations. An exhaustive efficiency study is performed to show the competitiveness of these methods. Finally, we test some specific examples and explore the effect that this predictor may have on the convergence set by setting a dynamical study.Ministerio de Economia y Competitividad de Espana, Grant/Award Number: MTM2014-52016-C2-2-P; Generalitat Valenciana Prometeo, Grant/Award Number: /2016/089Howk, CL.; Hueso, J.; Martínez Molada, E.; Teruel-Ferragud, C. (2018). A class of efficient high-order iterative methods with memory for nonlinear equations and their dynamics. Mathematical Methods in the Applied Sciences. 41(17):7263-7282. https://doi.org/10.1002/mma.4821S72637282411

Orthodontic treatment needs in the western region of Saudi Arabia: a research report

BACKGROUND: Evaluation of self perceived and actual need for orthodontic treatment helps in planning orthodontic services and estimating the required resources and man power. In the present study, the perceptive need as evaluated by patients and the actual need to orthodontic treatment, as assessed by orthodontists, were evaluated at two types of dental practices in the city of Jeddah using the Index of Orthodontic Treatment Need (IOTN). METHODS: A consecutive sample of 743 adults seeking orthodontic treatment at two different types of dental practices in Jeddah; King Abdulaziz University, Faculty of Dentistry (KAAU) (Free treatment) and two private dental polyclinics (PDP) (Paid treatment), was examined for orthodontic treatment need using the dental health component (DHC) of the IOTN. The self-perceived need for orthodontic treatment was also determined using the aesthetic component (AC) of the IOTN. The IOTN score and the incidence of each variable were calculated statistically. AC and DHC categories were compared using the Chi-Square and a correlation between them was assessed using Spearman's correlation test. AC and DHC were also compared between the two types of dental practices using the Chi-Square. RESULTS: The results revealed that among the 743 patients studied, 60.6% expressed no or slight need for treatment, 23.3% expressed moderate to borderline need and only16.1% thought they needed orthodontic treatment. Comparing these estimates to professional judgments, only 15.2% conformed to little or no need for treatment, 13.2% were assessed as in borderline need and 71.6% were assessed as in need for treatment (p < 0.001). Spearman's correlation test proved no correlation (r = -.045) between the two components. Comparing the AC and the DHC between the KAAU group and PDP group showed significant differences between the two groups (p < 0.001). CONCLUSION: Patient's perception to orthodontic treatment does not always correlate with professional assessment. The IOTN is a valid screening tool that should be used in orthodontic clinics for better services especially, in health centers that provide free treatment

An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis σ66 promoters

<p>Abstract</p> <p>Background</p> <p>Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from <it>Escherichia coli</it>. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between <it>Escherichia coli </it>and <it>Chlamydia trachomatis </it>are large enough to recommend an organism-specific modeling effort.</p> <p>Results</p> <p>Here we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model <it>Chlamydia trachomatis </it>σ⁶⁶promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for <it>Chlamydia trachomatis </it>RNA polymerase σ⁶⁶/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability.</p> <p>Conclusion</p> <p>This strategy and resulting model support the conjecture that DNA biophysical properties, along with RNA polymerase σ-factor/DNA binding collaboratively, contribute to a sequence's ability to promote transcription. This work provides a baseline model that can evolve as new <it>Chlamydia trachomatis </it>σ⁶⁶promoters are identified with assistance from the provided genome-wide predictions. The proposed methodology is ideal for organisms with few identified promoters and relatively small genomes.</p

Effect of CD26/dipeptidyl peptidase IV on Jurkat sensitivity to G2/M arrest induced by topoisomerase II inhibitors

CD26/dipeptidyl peptidase IV (DPPIV) is a surface antigen with multiple functions, including a role in T-cell activation and the development of certain human cancers. We previously demonstrated that CD26/DPPIV enhanced sensitivity of Jurkat cells to doxorubicin. We now show that expression of CD26/DPPIV enhanced sensitivity of CD26 Jurkat transfectants to G2–M arrest mediated by the antineoplastic agent etoposide. The increased sensitivity to etoposide-induced G2–M arrest was associated with disruption of cell cycle-related events, including hyperphosphorylation of p34cdc2 kinase, change in cdc25C expression and phosphorylation, and alteration in cyclin B1 expression. CD26/DPPIV-associated enhancement of doxorubicin and etoposide-induced G2–M arrest was also observed in serum-free media, suggesting an effect of CD26 on cell-derived processes rather than serum-derived factors. Importantly, our work elucidated a potential mechanism for the enhanced susceptibility of CD26-expressing Jurkat cells to the topoisomerase II inhibitors by demonstrating that CD26/DPPIV surface expression was associated with increased topoisomerase II α levels and enhanced enzyme activity. Besides being the first to show a functional association between the multifaceted molecule CD26 and the key cellular protein topoisomerase II α, our studies provide additional evidence of a potential role for CD26 in the treatment of selected malignancies

Mental disorders as risk factors: assessing the evidence for the Global Burden of Disease Study

Background: Mental disorders are associated with a considerable burden of disease as well as being risk factors for other health outcomes. The new Global Burden of Disease (GBD) Study will make estimates for both the disability and mortality directly associated with mental disorders, as well as the burden attributable to other health outcomes. Herein we discuss the process by which health outcomes in which mental disorders are risk factors are selected for inclusion in the GBD Study. We make suggestions for future research to strengthen the body of evidence for mental disorders as risk factors