On the balance energy and nuclear dynamics in peripheral heavy-ion collisions

We present here the system size dependence of balance energy for semi-central and peripheral collisions using quantum molecular dynamics model. For this study, the reactions of $Ne^{20}+Ne^{20}$, $Ca^{40}+Ca^{40}$, $Ni^{58}+Ni^{58}$, $Nb^{93}+Nb^{93}$, $Xe^{131}+Xe^{131}$ and $Au^{197}+Au^{197}$ are simulated at different incident energies and impact parameters. A hard equation of state along with nucleon-nucleon cross-sections between 40 - 55 mb explains the data nicely. Interestingly, balance energy follows a power law $\propto{A^{\tau}}$ for the mass dependence at all colliding geometries. The power factor $\tau$ is close to -1/3 in central collisions whereas it is -2/3 for peripheral collisions suggesting stronger system size dependence at peripheral geometries. This also suggests that in the absence of momentum dependent interactions, Coulomb's interaction plays an exceedingly significant role. These results are further analyzed for nuclear dynamics at the balance point.Comment: 13 pages, 9 figures Accepted in IJMPE (in press

Prevalence of Gram-negative Pathogens and their antimicrobial susceptibility in bacterial meningitis in pediatric cases

The present study was conducted to find out the prevalence and spectrum of Gram negative pathogens causing bacterial meningitis and their antimicrobial susceptibility pattern in a tertiary care hospital. The cerebrospinal fluid (CSF) (3-5 ml) was collected from 638 admitted children clinically suspected of septic meningitis. Bacterial isolates were identified and antimicrobial susceptibility was assessed by the Kirby-Bauer disk diffusion method. Of the 638 samples tested 102 (15.99%) were culture positive. Male to female (M:F) ratio was 1.62:1. The maximum incidence of 45 (44.12%) cases was found in children (1-12 yrs); in institutional deliveries the incidence was 58 (56.86%) cases. Further, the incidence of 51 cases was found from May to August. Escherichia coli (E. coli) were commonest, seen in 9 (25%) cases followed by Acinetobacter spp., Citrobacter spp. and Klebsiella spp. with 6 (16.67%) cases each. Enterobacter spp., Neisseria spp. and Pseudomonas aeruginosa were isolated in 3 (8.33%) cases each. E. coli, Acinetobacter spp, Citrobacter spp and Klebsiella spp isolates were 100% susceptible to meropenem, piperacillin-tazobactam and cefoperazone-sulbactam and 100% resistant to cotrimoxazole and tetracycline. All strains of Neisseria spp, Enterobacter spp and Pseudomonas spp. were 100% susceptible to meropenem followed by gatifloxacin. These were 100% resistant to tetracycline and cotrimoxazole. Neisseria spp. were also 100% susceptible to pristinamycin. In septic meningitis Gram negative organisms are less common (35.29%). Of the isolates, more common Gram negative isolates included E. coli, Acinetobacter Spp., Citrobacter Spp., and Klebsiella spp. and these isolates were 100% susceptible to meropenem, piperacillin-tazobacatam and cefoperazone-sulbactam. Hence, empirical therapy should be formulated according to antimicrobial susceptibility patterns

Machine Learning Regression Approach to the Nanophotonic Waveguide Analyses

Machine learning is an application of artificial intelligence that focuses on the development of computer algorithms which learn automatically by extracting patterns from the data provided. Machine learning techniques can be efficiently used for a problem with a large number of parameters to be optimized and also where it is infeasible to develop an algorithm of specific instructions for performing the task. Here, we combine the finite element simulations and machine learning techniques for the prediction of mode effective indices, power confinement and coupling length of different integrated photonics devices. Initially, we prepare a dataset using COMSOL Multiphysics and then this data is used for training while optimizing various parameters of the machine learning model. Waveguide width, height, operating wavelength, and other device dimensions are varied to record different modal solution parameters. A detailed study has been carried out for a slot waveguide structure to evaluate different machine learning model parameters including number of layers, number of nodes, choice of activation functions, and others. After training, this model is used to predict the outputs for new input device specifications. This method predicts the output for different device parameters faster than direct numerical simulation techniques. Absolute percentage error of less than 5% in predicting an output has been obtained for slot, strip and directional waveguide coupler designs. This study pave the step towards using machine learning based optimization techniques for integrated silicon photonics devices

Machine learning approach for computing optical properties of a photonic crystal fiber

Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and confinement loss for a solid-core PCF. These machine learning algorithms based on artificial neural networks are able to make accurate predictions of above-mentioned optical properties for usual parameter space of wavelength ranging from 0.5-1.8 µm, pitch from 0.8-2.0 µm, diameter by pitch from 0.6-0.9 and number of rings as 4 or 5 in a silica solid-core PCF. We demonstrate the use of simple and fast-training feed-forward artificial neural networks that predicts the output for unknown device parameters faster than conventional numerical simulation techniques. Computation runtimes required with neural networks (for training and testing) and Lumerical MODE solutions are also compared

Actin cortex architecture regulates cell surface tension

Animal cell shape is largely determined by the cortex, a thin actin network underlying the plasma membrane in which myosin-driven stresses generate contractile tension. Tension gradients result in local contractions and drive cell deformations. Previous cortical tension regulation studies have focused on myosin motors. Here, we show that cortical actin network architecture is equally important. First, we observe that actin cortex thickness and tension are inversely correlated during cell-cycle progression. We then show that the actin filament length regulators CFL1, CAPZB and DIAPH1 regulate mitotic cortex thickness and find that both increasing and decreasing thickness decreases tension in mitosis. This suggests that the mitotic cortex is poised close to a tension maximum. Finally, using a computational model, we identify a physical mechanism by which maximum tension is achieved at intermediate actin filament lengths. Our results indicate that actin network architecture, alongside myosin activity, is key to cell surface tension regulation

Iron Deposition following Chronic Myocardial Infarction as a Substrate for Cardiac Electrical Anomalies: Initial Findings in a Canine Model

Purpose: Iron deposition has been shown to occur following myocardial infarction (MI). We investigated whether such focal iron deposition within chronic MI lead to electrical anomalies. Methods: Two groups of dogs (ex-vivo (n = 12) and in-vivo (n = 10)) were studied at 16 weeks post MI. Hearts of animals from ex-vivo group were explanted and sectioned into infarcted and non-infarcted segments. Impedance spectroscopy was used to derive electrical permittivity () and conductivity (). Mass spectrometry was used to classify and characterize tissue sections with (IRON+) and without (IRON-) iron. Animals from in-vivo group underwent cardiac magnetic resonance imaging (CMR) for estimation of scar volume (late-gadolinium enhancement, LGE) and iron deposition (T2*) relative to left-ventricular volume. 24-hour electrocardiogram recordings were obtained and used to examine Heart Rate (HR), QT interval (QT), QT corrected for HR (QTc) and QTc dispersion (QTcd). In a fraction of these animals (n = 5), ultra-high resolution electroanatomical mapping (EAM) was performed, co-registered with LGE and T2* CMR and were used to characterize the spatial locations of isolated late potentials (ILPs). Results: Compared to IRON- sections, IRON+ sections had higher, but no difference in. A linear relationship was found between iron content and (p1.5%)) with similar scar volumes (7.28%±1.02% (Iron (1.5%)), p = 0.51) but markedly different iron volumes (1.12%±0.64% (Iron (1.5%)), p = 0.02), QT and QTc were elevated and QTcd was decreased in the group with the higher iron volume during the day, night and 24-hour period (p<0.05). EAMs co-registered with CMR images showed a greater tendency for ILPs to emerge from scar regions with iron versus without iron. Conclusion: The electrical behavior of infarcted hearts with iron appears to be different from those without iron. Iron within infarcted zones may evolve as an arrhythmogenic substrate in the post MI period

What is the real impact of acute kidney injury?

Background: Acute kidney injury (AKI) is a common clinical problem. Studies have documented the incidence of AKI in a variety of populations but to date we do not believe the real incidence of AKI has been accurately documented in a district general hospital setting. The aim here was to describe the detected incidence of AKI in a typical general hospital setting in an unselected population, and describe associated short and long-term outcomes. Methods: A retrospective observational database study from secondary care in East Kent (adult catchment population of 582,300). All adult patients (18 years or over) admitted between 1st February 2009 and 31st July 2009, were included. Patients receiving chronic renal replacement therapy (RRT), maternity and day case admissions were excluded. AKI was defined by the acute kidney injury network (AKIN) criteria. A time dependent risk analysis with logistic regression and Cox regression was used for the analysis of in-hospital mortality and survival. Results: The incidence of AKI in the 6 month period was 15,325 pmp/yr (adults) (69% AKIN1, 18% AKIN2 and 13% AKIN3). In-hospital mortality, length of stay and ITU utilisation all increased with severity of AKI. Patients with AKI had an increase in care on discharge and an increase in hospital readmission within 30 days. Conclusions: This data comes closer to the real incidence and outcomes of AKI managed in-hospital than any study published in the literature to date. Fifteen percent of all admissions sustained an episode of AKI with increased subsequent short and long term morbidity and mortality, even in those with AKIN1. This confers an increased burden and cost to the healthcare economy, which can now be quantified. These results will furnish a baseline for quality improvement projects aimed at early identification, improved management, and where possible prevention, of AKI

Rapid, ultra low coverage copy number profiling of cell-free DNA as a precision oncology screening strategy.

Current cell-free DNA (cfDNA) next generation sequencing (NGS) precision oncology workflows are typically limited to targeted and/or disease-specific applications. In advanced cancer, disease burden and cfDNA tumor content are often elevated, yielding unique precision oncology opportunities. We sought to demonstrate the utility of a pan-cancer, rapid, inexpensive, whole genome NGS of cfDNA approach (PRINCe) as a precision oncology screening strategy via ultra-low coverage (~0.01x) tumor content determination through genome-wide copy number alteration (CNA) profiling. We applied PRINCe to a retrospective cohort of 124 cfDNA samples from 100 patients with advanced cancers, including 76 men with metastatic castration-resistant prostate cancer (mCRPC), enabling cfDNA tumor content approximation and actionable focal CNA detection, while facilitating concordance analyses between cfDNA and tissue-based NGS profiles and assessment of cfDNA alteration associations with mCRPC treatment outcomes. Therapeutically relevant focal CNAs were present in 42 (34%) cfDNA samples, including 36 of 93 (39%) mCRPC patient samples harboring AR amplification. PRINCe identified pre-treatment cfDNA CNA profiles facilitating disease monitoring. Combining PRINCe with routine targeted NGS of cfDNA enabled mutation and CNA assessment with coverages tuned to cfDNA tumor content. In mCRPC, genome-wide PRINCe cfDNA and matched tissue CNA profiles showed high concordance (median Pearson correlation = 0.87), and PRINCe detectable AR amplifications predicted reduced time on therapy, independent of therapy type (Kaplan-Meier log-rank test, chi-square = 24.9, p &lt; 0.0001). Our screening approach enables robust, broadly applicable cfDNA-based precision oncology for patients with advanced cancer through scalable identification of therapeutically relevant CNAs and pre-/post-treatment genomic profiles, enabling cfDNA- or tissue-based precision oncology workflow optimization

Rapidity distribution as a probe for elliptical flow at intermediate energies

Interplay between the spectator and participant matter in heavy-ion collisions is investigated within isospin dependent quantum molecular dynamics (IQMD) model in term of rapidity distribution of light charged particles. The effect of different types and size rapidity distributions is studied in elliptical flow. The elliptical flow patterns show important role of the nearby spectator matter on the participant zone. This role is further explained on the basis of passing time of the spectator and expansion time of the participant zone. The transition from the in-plane to out-of-plane is observed only when the mid-rapidity region is included in the rapidity bin, otherwise no transition occurs. The transition energy is found to be highly sensitive towards the size of the rapidity bin, while weakly on the type of the rapidity distribution. The theoretical results are also compared with the experimental findings and are found in good agreement.Comment: 8 figure

Patients' satisfaction with information at discharge

Background: Adequate patient knowledge and engagement with their condition and its management can reduce re-hospitalisations and improve outcomes after acute admission for circulatory system disease. Aim: To evaluate the perceptions of cardio- or cerebrovascular patients of their satisfaction with discharge processes and to determine if this differs by demographic groups. Methods: A sample of 536 eligible public hospital inpatients was extracted from a consumer experience surveillance system. Questions relating to the discharge process were analysed using descriptive statistics to compare patient satisfaction levels against demographic variables. Results: Dissatisfaction rates were highest within the ‘Written information provided’ (37.8%) and ‘Danger signals communicated’ (34.7%) categories. Women and people aged ≥80 were more likely to express dissatisfaction. Conclusion: Although respondents were largely satisfied, there are important differences in the characteristics of those that were dissatisfied. The communication of important discharge information to older people and women was less likely to meet their perceived needs