Impurity effect of Lambda hyperon on collective excitations of atomic nuclei

Taking the ground state rotational band in $^{24}$Mg as an example, we investigate the impurity effect of $\Lambda$ hyperon on collective excitations of atomic nuclei in the framework of non-relativistic energy density functional theory. To this end, we take into account correlations related to the restoration of broken symmetries and fluctuations of collective variables by solving the eigenvalue problem of a five-dimensional collective Hamiltonian for quadrupole vibrational and rotational degrees of freedom. The parameters of the collective Hamiltonian are determined with constrained mean-field calculations for triaxial shapes using the SGII Skyrme force. We compare the low-spin spectrum for $^{24}$Mg with the spectrum for the same nucleus inside $^{25}_{\Lambda}$Mg. It is found that the $\Lambda$ hyperon stretches the ground state band and reduces the $B(E2:2^+_1 \rightarrow 0^+_1)$ value by $\sim 9$, mainly by softening the potential energy surface towards the spherical shape, even though the shrinkage effect on the average proton radius is only $\sim0.5$.Comment: 16 pages, 5 figures, and 1 tabl

C1 and C2 vertebrae osteomyelitis: a misleading presentation leading to a fatal outcome

Cervical vertebral osteomyelitis is rare. While an early and correct diagnosis is critical to prevent catastrophic neurological injury, the diagnosis of cervical vertebral osteomyelitis is often difficult because of its rarity and variable symptoms. We present a case of C1 and C2 vertebrae osteomyelitis with a misleading presentation and its fatal outcome

Structured Near-Optimal Channel-Adapted Quantum Error Correction

We present a class of numerical algorithms which adapt a quantum error correction scheme to a channel model. Given an encoding and a channel model, it was previously shown that the quantum operation that maximizes the average entanglement fidelity may be calculated by a semidefinite program (SDP), which is a convex optimization. While optimal, this recovery operation is computationally difficult for long codes. Furthermore, the optimal recovery operation has no structure beyond the completely positive trace preserving (CPTP) constraint. We derive methods to generate structured channel-adapted error recovery operations. Specifically, each recovery operation begins with a projective error syndrome measurement. The algorithms to compute the structured recovery operations are more scalable than the SDP and yield recovery operations with an intuitive physical form. Using Lagrange duality, we derive performance bounds to certify near-optimality.Comment: 18 pages, 13 figures Update: typos corrected in Appendi

Optimum Quantum Error Recovery using Semidefinite Programming

Quantum error correction (QEC) is an essential element of physical quantum information processing systems. Most QEC efforts focus on extending classical error correction schemes to the quantum regime. The input to a noisy system is embedded in a coded subspace, and error recovery is performed via an operation designed to perfectly correct for a set of errors, presumably a large subset of the physical noise process. In this paper, we examine the choice of recovery operation. Rather than seeking perfect correction on a subset of errors, we seek a recovery operation to maximize the entanglement fidelity for a given input state and noise model. In this way, the recovery operation is optimum for the given encoding and noise process. This optimization is shown to be calculable via a semidefinite program (SDP), a well-established form of convex optimization with efficient algorithms for its solution. The error recovery operation may also be interpreted as a combining operation following a quantum spreading channel, thus providing a quantum analogy to the classical diversity combining operation.Comment: 7 pages, 3 figure

Breast cancer data analysis for survivability studies and prediction

© 2017 Elsevier B.V. Background Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. Objective The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer survivability in presence of missing data, (ii) providing better insights into factors associated with patient survivability, and (iii) establishing cohorts of patients that share similar properties. Methods Unsupervised data mining methods viz. the self-organising map (SOM) and density-based spatial clustering of applications with noise (DBSCAN) is used to create patient cohort clusters. These clusters, with associated patterns, were used to train multilayer perceptron (MLP) model for improved patient survivability analysis. A large dataset available from SEER program is used in this study to identify patterns associated with the survivability of breast cancer patients. Information gain was computed for the purpose of variable selection. All of these methods are data-driven and require little (if any) input from users or experts. Results SOM consolidated patients into cohorts of patients with similar properties. From this, DBSCAN identified and extracted nine cohorts (clusters). It is found that patients in each of the nine clusters have different survivability time. The separation of patients into clusters improved the overall survival prediction accuracy based on MLP and revealed intricate conditions that affect the accuracy of a prediction. Conclusions A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and survivability. The survivability prediction accuracy of a MLP is improved by using identified patient cohorts as opposed to using raw historical data. Analysis of variable values in each cohort provide better insights into survivability of a particular subgroup of breast cancer patients

Managing the patient with episodic sinus tachycardia and orthostatic intolerance

Patients with episodic sinus tachycardia and associated orthostatic intolerance present a diagnostic and management dilemma to the clinician. We define this group of disorders to include sinus node reentrant tachycardia (SNRT), inappropriate sinus tachycardia (IAST), and postural orthostatic tachycardia syndrome (POTS). After a brief review of the current understanding of the pathophysiology and epidemiology of this group of disorders, we focus on the diagnosis and management of IAST and POTS. Our approach attempts to recognize the considerable overlap in pathophysiology and clinical presentation between these two heterogeneous conditions. Thus, we focus on a mechanism-based workup and therapeutic approach. Sinus tachycardia related to identifiable causes should first be ruled out in these patients. Next, a basic cardiovascular and autonomic workup is suggested to exclude structural heart disease, identify a putative diagnosis, and guide therapy. We review both nonpharmacologic and pharmacologic therapy, with a focus on recent advances. Larger randomized control trials and further mechanistic studies will help refine management in the future

Understanding urban inequalities in children's linear growth outcomes: a trend and decomposition analysis of 39,049 children in Bangladesh (2000-2018)

BACKGROUND: Despite significant progress in reducing child undernutrition, Bangladesh remains among the top six countries globally with the largest burden of child stunting and has disproportionately high stunting prevalence among the urban poor. We use population representative data to identify key predictors of child stunting in Bangladesh and assess their contributions to linear growth differences observed between urban poor and non-poor children. METHODS: We combined six rounds of Demographic and Health Survey data spanning 2000-2018 and used official poverty rates to classify the urban population into poor and non-poor households. We identified key stunting determinants using stepwise selection method. Regression-decomposition was used to quantify contributions of these key determinants to poverty-based intra-urban differences in child linear growth status. RESULTS: Key stunting determinants identified in our study predicted 84% of the linear growth difference between urban poor and non-poor children. Child's place of birth (27%), household wealth (22%), maternal education (18%), and maternal body mass index (11%) were the largest contributors to the intra-urban child linear growth gap. Difference in average height-for-age z score between urban poor and non-poor children declined by 0.31 standard deviations between 2000 and 2018. About one quarter of this observed decrease was explained by reduced differentials between urban poor and non-poor in levels of maternal education and maternal underweight status. CONCLUSIONS: Although the intra-urban disparity in child linear growth status declined over the 2000-2018 period, socioeconomic gaps remain significant. Increased nutrition-sensitive programs and investments targeting the urban poor to improve girls' education, household food security, and maternal and child health services could aid in further narrowing the remaining linear growth gap