Flow of shear response functions in hyperscaling violating Lifshitz theories

We study the flow equations of the shear response functions for hyperscaling violating Lifshitz (hvLif) theories, with Lifshitz and hyperscaling violating exponents $z$ and $\theta$. Adapting the membrane paradigm approach of analysing response functions as developed by Iqbal and Liu, we focus specifically on the shear gravitational modes which now are coupled to the perturbations of the background gauge field. Restricting to the zero momenta sector, we make further simplistic assumptions regarding the hydrodynamic expansion of the perturbations. Analysing the flow equations shows that the shear viscosity at leading order saturates the Kovtun-Son-Starinets (KSS) bound of $\frac{1}{4\pi}$. When $z=d_i-\theta$, ($d_i$ being the number of spatial dimension in the dual field theory) the first-order correction to shear viscosity exhibits logarithmic scaling, signalling the emergence of a scale in the UV regime for this class of hvLif theories. We further show that the response function associated to the gauge field perturbations diverge near the boundary when $z>d_i+2-\theta$. This provides a holographic understanding of the origin of such a constraint and further vindicates results obtained in previous works that were obtained through near horizon and quasinormal mode analysis.Comment: Includes new subsection on Markovianity index and breakdown of hydrodynamic expansion; Matches with published version; 19 + 3 page

Dexamethasone Attenuates Hyperexcitability Provoked by Experimental Febrile Status Epilepticus.

The role of neuroinflammation in the mechanisms of epilepsy development is important because inflammatory mediators provide tractable targets for intervention. Inflammation is intrinsically involved in the generation of childhood febrile seizures (FSs), and prolonged FS [febrile status epilepticus (FSE)] precedes a large proportion of adult cases of temporal lobe epilepsy (TLE). As TLE is often refractory to therapy and is associated with serious cognitive and emotional problems, we investigated whether its development can be prevented using anti-inflammatory strategies. Using an immature rat model of FSE [experimental FSE (eFSE)], we administered dexamethasone (DEX), a broad anti-inflammatory agent, over 3 d following eFSE. We assessed eFSE-provoked hippocampal network hyperexcitability by quantifying the presence, frequency, and duration of hippocampal spike series, as these precede and herald the development of TLE-like epilepsy. We tested whether eFSE provoked hippocampal microgliosis, astrocytosis, and proinflammatory cytokine production in male and female rats and investigated blood-brain barrier (BBB) breaches as a potential contributor. We then evaluated whether DEX attenuated these eFSE sequelae. Spike series were not observed in control rats given vehicle or DEX, but occurred in 41.6% of eFSE-vehicle rats, associated with BBB leakage and elevated hippocampal cytokines. eFSE did not induce astrocytosis or microgliosis but provoked BBB disruption in 60% of animals. DEX significantly reduced spike series prevalence (to 7.6%) and frequency, and abrogated eFSE-induced cytokine production and BBB leakage (to 20%). These findings suggest that a short, postinsult intervention with a clinically available anti-inflammatory agent potently attenuates epilepsy-predicting hippocampal hyperexcitability, potentially by minimizing BBB disruption and related neuroinflammation

Efficiently correlating complex events over live and archived data streams

Correlating complex events over live and archived data streams, which we call Pattern Correlation Queries (PCQs), provides many benefits for domains which need real-time forecasting of events or identification of causal dependencies, while handling data at high rates and in massive amounts, like in financial or medical settings. Existing work has focused either on complex event processing over a single type of stream source (i.e., either live or archived), or on simple stream correlation queries (e.g., live events trigerring a database lookup). In this paper, we specifically focus on recency-based PCQs and provide clear, useful, and optimizable semantics for them. PCQs raise a number of challenges in optimizing data management and query processing, which we address in the setting of the DejaVu complex event processing system. More specifically, we propose three complementary optimizations including recent in-put buffering, query result caching, and join source ordering. Fur-thermore, we capture the relevant query processing tradeoffs in a cost model. An extensive performance study on synthetic and real-life data sets not only validates this cost model, but also shows that our optimizations are very effective, achieving more than two orders magnitude throughput improvement and much better scala-bility compared to a conventional approach

Knowledge, attitude and practice of Tanta University medical students towards hepatitis B and C

Background: Egypt lies among the world’s highest prevalence rates of HCV and intermediate levels of HBV infection. The objectives of the study were detection of the knowledge, attitude and practice of Medical Students of Tanta University towards hepatitis B and C.Methods: This was a cross-sectional study, conducted in The Faculty of Medicine, Tanta University, Egypt; from 15th October 2013 to 15th of January 2014.Results: The study included 185 Students; their ages ranged between 17 to 28 years with a mean 20±1.731years. Sixty percent of students were males and 65% were urban residents. 50.8% of the participants were in the basic level of the academic study. More than half (57.85%) of the participants had sufficient knowledge, 77.3% of them had a positive attitude towards hepatitis C and B and more than two-thirds (68.1%) showed good practice. A significant association occurred between a positive attitude and good practice. Sufficient knowledge was significantly recorded among older students, females, urban residents and the clinical stage students. The most frequent sources of student information were family or friends, internet followed by TV or radio, healthcare workers, and newspapers.Conclusions: The students had reasonable knowledge, positive attitude and good practices towards B and C viral hepatitis. Areas of insufficient knowledge needed to be reinforced included some modes of transmission, complications, and treatment for B and C viral hepatitis

A possible optical counterpart of the X-ray source NuSTARJ053449+2126.0

In this work, we report the observation of a possible optical counterpart to the recently discovered X-ray source NuSTAR J053449+2126.0. To search for an optical counterpart of NuSTAR J053449+2126.0 (J0534 in short), we observed the source with the 1.5-m Telescope (RTT150). Using the B, V, R, and I images of J0534, we detected the possible optical counterpart of J0534 and determined, based on our spectral analysis, the source distance for the first time. J0534 could be a high-redshift member of an Active Galactic Nucleus (AGN) sub-group identified as a quasar. Our analysis favours an accreting black hole of mass $\sim 7\times 10^8 M_{\odot}$ as a power supply for the quasar in J0534. Further observations in optical and other wavelengths are needed to confirm its nature.Comment: 7 pages, 4 figure

Comparative analysis of maternal and neonatal outcomes between elective and emergency caesarean section at a single tertiary hospital: a retrospective COHORT study

Background: Caesarean section rates have been increasing worldwide despite it’s known complications. The aim of this study was to determine maternal and neonatal complications related to caesarean section at Sultan Qaboos University Hospital (SQUH) and to compare the outcomes between emergency and elective caesarean sections. Methods: This retrospective cohort study was conducted in the department of obstetrics and gynecology at SQUH from 1st January 2016 to 31st December 2016. This comparative study involved 300 women who underwent caesarean section, 150 in elective caesarean section group and 150 in emergency caesarean section group. Results: The mean maternal age was 29.66 (±4.96) and 33.22 (±4.63) years in the elective and emergency caesarean section groups respectively (p=001). The main risk factor for both the groups was maternal diabetes and the most common indication was previous caesarean section. Hypotension related anesthetic complication was noted more in elective caesarean section (15.3%) than in emergency caesarean section group (4.0%) with p value=0.002. Post-partum fever was seen in 12.0% of women in emergency group as compared to 4% in elective group (p=0.019). Anemia was observed in 79.2% and 65.3% in emergency and elective groups respectively (p=0.011). Respiratory distress syndrome and transient tachypnea of the newborn were the main neonatal complications in both groups. Conclusions: There was no significant difference between emergency and elective caesarean section related maternal and neonatal complications except for transient intraoperative hypotension, maternal postoperative febrile morbidity and anemia. Future prospective studies including larger sample size and multiple centers is recommended.

Enhancing Breast Cancer Prediction through Deep Learning and Comparative Analysis of Gene Expression and DNA Methylation Data using Convolutional Neural Networks

Recent advances in the production of statistics have resulted in an exponential increase in the number of facts, ushering in a whole new era dominated by very large facts. Conventional machine-learning algorithms are unable to handle the most recent aspects of huge data. This is a fact.  In order to make an accurate prognosis of breast cancer, researchers employ and evaluate three distinct computer programmes called Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). Within the context of huge statistics, we explore the question of how breast cancer may be predicted in this particular research. Gene expression and DNA methylation are both taken into consideration as part of the analysis (GE and DM, respectively). The purpose of the work that we are doing is to increase the capacity of the Deep Learning algorithms that are now being used for typing by applying each dataset individually and together. As a result of this decision, the platform of choice is MATLAB. In the process of breast cancer prediction, the Convolutional Neural Network (CNN) algorithm is used. Comparisons of GE, DM, and GE and DM are carried out with the help of this method. The results of the CNN algorithm are compared to those of the RF algorithm. According to findings of the experiments, the scaled system that was presented works better than the other classifiers. This is due to the fact that using the GE dataset; it acquired the best accuracy at the lowest cost