Improvement of the mechanism of congestion avoidance in mobile networks

Mobile ad hoc network congestion control is a significant problem. Standard mechanism for congestion control (TCP), the ability to run certain features of a wireless network, several mutations are not common. In particular, the enormous changes in the network topology and the joint nature of the wireless network. It also creates significant challenges in mobile ad hoc networks (MANET), density is one of the most important limitations that disrupts the function of the entire network, after multi-path routing can load balance in relation to the single-path routing in ad hoc networks better, so the traffic division multiple routs congestion is reduced. This study is a multi-path load balancing and congestion control based on the speed of rate control mechanism to avoid congestion in the network provides communication flows. Given such a speed control method that is consistent is that the destination node copy speed is estimated at intermediate nodes and its reflection in the In the forward direction confirmation to the sender sends a packet, therefore the rate quickly estimate The results of the simulation has been set to demonstrate that a given method better package delivery speed and expanded capacity and density to be effective checks congestion control method is better than The result traditional

Inhibition of Ras activity coordinates cell fusion with cell-cell contact during yeast mating.

In the fission yeast Schizosaccharomyces pombe, pheromone signaling engages a signaling pathway composed of a G protein-coupled receptor, Ras, and a mitogen-activated protein kinase (MAPK) cascade that triggers sexual differentiation and gamete fusion. Cell-cell fusion requires local cell wall digestion, which relies on an initially dynamic actin fusion focus that becomes stabilized upon local enrichment of the signaling cascade on the structure. We constructed a live-reporter of active Ras1 (Ras1-guanosine triphosphate [GTP]) that shows Ras activity at polarity sites peaking on the fusion structure before fusion. Remarkably, constitutive Ras1 activation promoted fusion focus stabilization and fusion attempts irrespective of cell pairing, leading to cell lysis. Ras1 activity was restricted by the guanosine triphosphatase-activating protein Gap1, which was itself recruited to sites of Ras1-GTP and was essential to block untimely fusion attempts. We propose that negative feedback control of Ras activity restrains the MAPK signal and couples fusion with cell-cell engagement

Partner Search Strategy and Mechanisms of Cell Polarization during Fission Yeast Mating

In the living world there are many kinds of self-organized biological patterns at different length scales from molecules to cells to animal communities. Cell polarity is a process of self-organization of cellular components into a highly asymmetric structure which leads to asymmetry of cell shape, structure or function. Cell polarization during fission yeast mating, the subject of this thesis, is part of a broader topic of cell polarization which is fundamental in biophysics and cell biology. I used computational modeling to address this topic in mating fission yeast cells.Mating fission yeast cells use diffusion-based molecular communication to find the closest potential opposite mating partner. Each mating type secretes its own specific pheromone peptide to make a pheromone concertation field in its vicinity to communicate with the neighboring opposite mating type cells. Fission yeast cells sense the pheromone gradient by binding of opposite mating type pheromones to their cognate receptors on the cell surface (Merlini et al, 2013). Initiation of cell polarization toward a mating partner in fission yeast cells involves accumulation of signaling proteins, mainly small GTPases such as Cdc42 and Ras1, into a polarity zone close to the mating partner (Park & Bi, 2007). This eventually results in directional growth of a mating projection (shmoo) from both partners toward one another and fusing with each other. Recently Bendezu et al showed that the establishment of the polarity zone in fission yeast is independent of gradient sensing (Bendezu & Martin, 2013). They demonstrated that prior to shmoo formation Cdc42, the main regulator of cell growth, accumulates into a dynamic polarity zone that explores the cell periphery in discrete jumps and stabilizes close to the opposite mating partner which is also a location with high pheromone concentration. Besides it has been previously shown that establishment of the polarity axis, accumulation of signaling lipids (such as PIP3) and signaling proteins (such as Rac and Rho) in the direction of migration, in larger motile eukaryotic model organisms like neutrophils and Dictyostelium discoideum amoebae which can migrate toward the chemical gradients is also independent of gradient sensing mechanism (Insall, 2010). In this thesis, I focus on studying the role of the polarity patch as well as the underlying mechanism for the patch formation, exploration and stabilization in mating fission yeast cells.First, we studied the role of the polarity patch in the mating selection mechanism in fission yeast cells. By developing 2D simulations mimicking a mating experiment consisting of a field of opposite mating type cells on a thin agarose pad, the effect of range of the pheromone gradient on the efficiency of the final number of paired cells was studied. The shape of a pheromone gradient field from neighboring cells depends on the diffusion coefficient of each pheromone type, the sites of pheromone secretion and the concentration profiles of the secreted proteases around the cell that degrade the pheromones (Arkowitz, 2009). We found that the combination of a local secretion and local sensing of pheromones from the polarity sites, short decay length of pheromone and pheromone-concentration-dependent scaling of the polarity patch lifetime results in the maximal number of paired cells. This study provided evidence that fission yeast applies a temporal averaging sensing strategy by employing the randomly exploring polarity patch that biases it random walk towards the opposite mating partner. These results were tested experimentally by our collaborator Dr. Laura Merlini from the Martin laboratory at the University of Lausanne.Second, we looked into the underlying mechanism of polarity patch formation, exploration and stabilization in mating fission yeast cells. Particularly, we studied the dynamic regulation of Ras1, the only Ras GTPase homolog in fission yeast, through positive and negative feedbacks. Ras1 is an upstream regulator of Cdc42 and is essential for polarity establishment and mating (Merlini et al, 2013). Like other GTPases it exists in inactive form of guanosine diphosphate (GDP) and active form of guanosine triphosphate (GTP) states. We developed a 3D reaction-diffusion model taking into account the diffusion and the interactions between Ras1-GDP, Ras1-GTP and Gap1, the only GTPase-activating protein (GAP) for Ras1, on the curved geometry of the cell membrane. By implementing an autocatalytic positive feedback and a negative feedback through the Ras1-GTP recruited GAP, Gap1, the model captured the appearance and disappearance behavior of the patch at random locations on the cell cortex. To estimate the diffusion coefficients and membrane dissociation rates of each component, we analyzed and used 3D simulations to fit the data from the Fluoresce Recovery After Photo bleaching (FRAP) experiments performed by Dr. Laura Merlini. Furthermore, we investigated the switch from exploration to stabilization of the Ras1 patch upon sensing of higher concentrations of pheromone in its vicinity. The patch in this model becomes stabilized at positions with higher rate of positive feedback, which may result from higher pheromone concentrations in its vicinity. The model predicts that the patch size and number can be regulated through positive and negative feedback rates. In simulations an increase in the negative feedback rates results in narrower patches and an increase in positive feedback results in multiple simultaneous patches. These results were then tested and supported experimentally by our collaborator Dr. Laura Merlini

Evaluation of the prevalence of typical and atypical enteropathogenic Escherichia coli isolated from stool specimens of patients with diarrhea admitted to Tehran Children's Hospital by the PCR Method

Background: Infectious diarrheal diseases are a major cause of death in community, especially in children. Enteropathogenic Escherichia coli (EPEC) are considered as a major cause of children's diarrhea in developing countries. The aim of this study was to evaluate the prevalence of both typical Enteropathogenic (tEPEC) and atypical Enteropathogenic (aEPEC) E. coli isolated from patients admitted to the children's hospital in Tehran by the PCR method. Materials and Methods: In this cross-sectional study, a total of 157 children diarrheal samples were collected from February 2016 to August 2017 and were sent to the microbiology department in the School of Public Health in Tehran University of Medical Sciences for testing. The identification of isolates was performed by conventional biochemical tests. The typical and atypical E. coli isolates were identified for the presence of eae, sxt1, sxt2 genes, and bfp A by the PCR method. The drug resistance patterns of isolated EPEC were tested by the agar disk diffusion method. The antibiotics used were amoxicillin-clavulanic, ampicillin, gentamicin, trimethoprim- sulfamethoxazole, ciprofloxacin, cefepim, Nitrofurantoin and imipenem. Results: Out of 101 E. coli isolates, 7 were identified as EPEC. All the isolated strains carried eae but not stx1, stx2, and bfp A genes. Also, 100% of the isolates were resistant to amoxicillin-clavulanic and ampicillin. Conclusion: A high prevalence of EPEC in children can be considered as a threat to the children's health. In this study, all the isolates were aEPEC

Fission Yeast Polarization: Modeling Cdc42 Oscillations, Symmetry Breaking, and Zones of Activation and Inhibition

Cells polarize for growth, motion, or mating through regulation of membrane-bound small GTPases between active GTP-bound and inactive GDP-bound forms. Activators (GEFs, GTP exchange factors) and inhibitors (GAPs, GTPase activating proteins) provide positive and negative feedbacks. We show that a reaction–diffusion model on a curved surface accounts for key features of polarization of model organism fission yeast. The model implements Cdc42 membrane diffusion using measured values for diffusion coefficients and dissociation rates and assumes a limiting GEF pool (proteins Gef1 and Scd1), as in prior models for budding yeast. The model includes two types of GAPs, one representing tip-localized GAPs, such as Rga3; and one representing side-localized GAPs, such as Rga4 and Rga6, that we assume switch between fast and slow diffusing states. After adjustment of unknown rate constants, the model reproduces active Cdc42 zones at cell tips and the pattern of GEF and GAP localization at cell tips and sides. The model reproduces observed tip-to-tip oscillations with periods of the order of several minutes, as well as asymmetric to symmetric oscillations transitions (corresponding to NETO “new end take off”), assuming the limiting GEF amount increases with cell size

The success of simultaneous balloon dacryoplasty and stenting in failed congenital nasolacrimal duct intubations and its indications

Background: Congenital nasolacrimal duct obstruction (CNLDO) is one of the most prevalent orbital diseases in children and treatment of recalcitrant cases is always challenging. The purpose of this study is to identify the effectiveness of balloon dacryoplasty and stenting in persistent congenital nasolacrimal duct obstruction following previous intubation of nasolacrimal duct. Methods: Our study was an interventional study from January 2015 to January 2018 on 16 lacrimal systems of 11 patients (5 males and 6 females) with congenital obstruction of the lacrimal duct (CNLDO) and a history of unsuccessful probing and stenting, in Farabi Hospital of Tehran (affiliated to Tehran University of Medical Sciences). Children who presented to our hospital and had previously been probed with or without intubation by another surgeon first underwent reprobing and re-intubation with a Crawford tube. Endoscopy of the nasolacrimal system was performed in suspected cases of false stent passage or in the presence of a history indicating nasal pathology. Crawford's Monoka tube was removed after two months. Balloon dacryoplasty with intubation was performed as the third surgery in cases who did not respond to probing and stenting after 3-6 months. The success after six months was evaluated using fluorescein dye disappearance test (FDDT) and also the resolution of the patients' symptoms. Results: The age of the patients was 67±35.01 months (range: 26-121). The site of the canalicular stenosis in our patients was in the common canaliculi or within 2-3 mm from it. After 6 months, surgery was successfully performed in 13 lacrimal systems (81.25%). One patient with congenital lacrimal duct obstruction and Down syndrome and two other patients did not respond to balloon dacryoplasty and stenting and subsequently underwent dacryocystorhinostomy (DCR). Conclusion: Balloon dacryoplasty combined with Monocrawford intubation is an effective surgical procedure that should be considered in cases of congenital nasolacrimal duct obstruction who have not responded to the probing and stenting of the lacrimal system

Enhancing residents’ neonatal resuscitation competency through team-based simulation training: an intervention educational study

Abstract Background Neonatal resuscitation training in a simulated delivery room environment is a new paradigm in pediatric medical education. The purpose of this research is to highlight team-based simulation as an effective method of teaching neonatal resuscitation to senior pediatric residents. Methods In an intervention educational study, we evaluated the impact of team-based simulation training in the development of neonatal resuscitation. A team consisting of a three-person group of senior pediatric residents performed neonatal resuscitation on a low-fidelity newborn simulator based on the stated scenario. Video-based structured debriefing was performed and followed by the second cycle of scenario and debriefing to evaluate the feasibility of conducting team-based simulation training in a lesser-resourced environment. Evaluation criteria included megacode scores which is a simulation performance checklist, pre-and post-test scores to evaluate residents’ knowledge and confidence, the survey checklist as a previously developed questionnaire assessing residents’ satisfaction, and debriefing from live and videotaped performances. Four months after the end of the training course, we measured the behavioral changes of the residents by conducting an OSCE test to evaluate post-training knowledge retention. Mean ± SD was calculated for megacode, satisfaction (survey checklist), and OSCE scores. Pre- and post-program gains were statistically compared. The first three levels of Kirkpatrick’s training effectiveness model were used to evaluate the progress of the program. Results Twenty-one senior residents participated in the team-based simulation. The mean ± SD of the megacode score was 35.6 ± 2.2. The mean ± SD of the overall satisfaction score for the evaluation of the first level of the Kirkpatrick model was 96.3 ± 3.7. For the evaluation of the second level of the Kirkpatrick model, the pre-posttest gain in overall confidence score had a statistically significant difference (P = 0.001). All residents obtained a passing grade in OSCE as an evaluation of the third level. Conclusions Team-based simulation training in neonatal resuscitation improves the knowledge, skills, and performance of pediatric residents and has a positive effect on their self-confidence and leadership skills. There is still a need to investigate the transfer of learning and abilities to real-life practice, and further research on cost-effectiveness and impact on patient outcomes is warranted

FRAP studies of Ras1 membrane diffusion.

<p>(A) Snapshots of FRAP of GFP-Ras1 at the sides of a WT cell (bleach region indicated by star). The scale bar is 1 μm. (B) Recovery of GFP-Ras1 at the sides of WT cells bleached over large (4.0 ± 0.2 μm) and small (1.5 ± 0.2 μm) regions, average of 5 cells. Measurements are made along the cell membrane, over the whole width of the bleached region. Continuous curves show fit by model with <i>D</i> = 0.15 and no cytoplasmic exchange. (C) Snapshots of FRAP of GFP-Ras1 at the sides of <i>GFP-ras1</i>^<i>Q66L</i> cells. The scale bar is 1 μm. (D) Same as panel B for GFP-Ras1 at the sides of <i>ras1</i>^<i>Q66L</i> cells and fit with <i>D</i> = 0.04 , and uniform cytoplasmic exchange rate 0.02 . (E) Plot showing acceptable set of diffusion coefficients and cytoplasmic exchange rates that can be fitted to the FRAP data of panels A-D for both large and small bleached regions for WT (blue squares) and <i>ras1</i>^<i>Q66L</i> (red circles). (F) Snapshots of FRAP simulations of wide (left) and narrow (right) regions.</p

Regions of dynamical behavior.

<p>Behavior observed in simulations for different values of , the GEF-mediated activation rate constant of Ras1-GTP, and , the Gap1-dependent hydrolysis rate constant of Ras1-GTP. Other free parameters were kept as shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006317#pcbi.1006317.t001" target="_blank">Table 1</a>. The yellow region represents simulations that mostly show a single patch oscillating however sometimes there are two patches that form simultaneously and then disappear at the same time or one after the other. One pixel* is one voronoi cell of the simulated surface.</p

Regulation of a stable patch size through positive and negative feedbacks.

<p>(A) Surface density profile over a 0.2 μm wide strip along the cell long axis going through the center of an exploring patch at its peak, for overall stronger negative feedback. Solid lines: Increase of Gap1 recruitment rate constant by 5 times; Dotted lines: Reference curves from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006317#pcbi.1006317.g004" target="_blank">Fig 4D</a> (values from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006317#pcbi.1006317.t001" target="_blank">Table 1</a>). (B) Surface density profile over a 0.2 μm wide strip along the cell long axis going through the center of two aligned patches peaking simultaneously, for stronger positive feedback. Solid lines: Increase of Ras1 activation rate constant by 1.6 times. Dotted lines: reference curves from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006317#pcbi.1006317.g004" target="_blank">Fig 4D</a>. (C) Surface density profile over a 0.2 μm wide strip along the cell long axis going through the center of a patch stabilized via stronger local positive feedback. Curves show effect of change with respect to values of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006317#pcbi.1006317.t001" target="_blank">Table 1</a>: (i) increase of Ras1 activation rate constant by 1.2 times; (ii) Increase of by 2 times; (iii) and increase of by 2 times and Gap1-dependent hydrolysis rate constant by 3 times, the latter over a 1.5 fold larger area than the cyan color region shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006317#pcbi.1006317.g006" target="_blank">Fig 6B</a>. (D) Snapshots of RasAct^GFP exploratory patch during early stages of mating in WT, Ste6 overexpression and Gap1 overexpression cells also expressing Myo52-tdTomato. The scale bar is 1 μm. Shown examples are not necessarily consecutively observed patches. (E) Patch size in WT (<i>n</i> = 404 patches in 23 cells), Ste6 overexpression (<i>n</i> = 467 patches in 28 cells) and Gap1 overexpression cells (<i>n</i> = 219 patches in 24 cells). Inset shows the average patch size and standard error calculated for WT (), Ste6 overexpression () and Gap1 overexpression cells (). The two sample t-test between WT and the mutants shows mean patch sizes are different at 0.01 significance level. (F) Normalized frequency of observed simultaneous patches in WT, Ste6 overexpression and Gap1 overexpression cells. We note that the patch intensity decreases in Gap1 overexpression cells (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006317#pcbi.1006317.s009" target="_blank">S6A Fig</a>) while our threshold for patch detection is the same as wt cells; thus we cannot exclude the possibility of more patches in Gap1 overexpression cells below the detection threshold.</p