Optimizing Antimicrobial Drug Use in Surgery: An Intervention Strategy in A Sudanese Hospital to Combat The Emergence of Bacterial Resistant

Background: Antimicrobial control programs are widely used to decrease antibiotic utilization, but effects on antimicrobial resistance and outcomes for patients remain controversial. The purpose of this study was to determine the impact of rotation of antibiotic classes used as empirical surgical prophylaxis on the emergence of bacterial resistance organisms and antibiotics drug use when compared with non-rotation period.Method: Three core, broad spectrum agents (cephalosporins, beta-lactam-inhibitors, and fluoroquinolones) were selected for inclusion in the quaternary rotation for 21 months, based on prior 8 months baseline data from GIT and urology surgical wards in Ibn Sina hospital. Intensivesurveillance done for patients admitted to the selected settings.Results: 1681 surveillance samples obtained from 2359 eligible inpatients admitted to hospital from Jan 2008 to May 2010. A significant reduction in the percentage of positive growth had been observed with antibiotic rotation for both wards from 65% and 49% in baseline to 59% and 33% inrotation (1) and 25% and 33% in rotation (2) in GIT and urology ward respectively (p` 0.0001). As general there was a divergent effect of the antimicrobial rotation on the prevalence of resistance among G+ve and G-ve bacteria.Conclusion: We concluded that antimicrobial drug use in surgical departments could be optimized after implementation of antimicrobial cycling policy, and associated in reduction in the incidence of infectious mortality and morbidity but stabilize antibiotic resistance, without significant reduction

Phase field modelling of grain boundary premelting using obstacle potentials

We investigate the multi-order parameter phase field model of Steinbach and Pezzolla [I. Steinbach, F. Pezzolla, A generalized field method for multiphase transformations using interface fields, Physica D 134 (1999) 385-393] concerning its ability to describe grain boundary premelting. For a single order parameter situation solid-melt interfaces are always attractive, which allows to have (unstable) equilibrium solid-melt-solid coexistence above the bulk melting point. The temperature dependent melt layer thickness and the disjoining potential, which describe the interface interaction, are affected by the choice of the thermal coupling function and the measure to define the amount of the liquid phase. Due to the strictly finite interface thickness also the interaction range is finite. For a multi-order parameter model we find either purely attractive or purely repulsive finite-ranged interactions. The premelting transition is then directly linked to the ratio of the grain boundary and solid-melt interfacial energy.Comment: 12 page

Pregnancy with extrahepatic portal venous hypertension

We are reporting a case of pregnancy with extrahepatic portal venous hypertension. Portal hypertension in pregnancy is an uncommon event. It presents a challenge to the obstetrician in management as physiological hemodynamic changes associated with pregnancy worsen with portal hypertension. Thus, increasing risk of life threatening complications like variceal haemorrhage and hepatic decompensation to many folds during pregnancy. Management requires knowledge of effects of portal hypertension on maternal and fetal outcome and vice-versa

Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning

For a federated learning model to perform well, it is crucial to have a diverse and representative dataset. However, the data contributors may only be concerned with the performance on a specific subset of the population, which may not reflect the diversity of the wider population. This creates a tension between the principal (the FL platform designer) who cares about global performance and the agents (the data collectors) who care about local performance. In this work, we formulate this tension as a game between the principal and multiple agents, and focus on the linear experiment design problem to formally study their interaction. We show that the statistical criterion used to quantify the diversity of the data, as well as the choice of the federated learning algorithm used, has a significant effect on the resulting equilibrium. We leverage this to design simple optimal federated learning mechanisms that encourage data collectors to contribute data representative of the global population, thereby maximizing global performance

Effect of quantum nuclear motion on hydrogen bonding

This work considers how the properties of hydrogen bonded complexes, D-H....A, are modified by the quantum motion of the shared proton. Using a simple two-diabatic state model Hamiltonian, the analysis of the symmetric case, where the donor (D) and acceptor (A) have the same proton affinity, is carried out. For quantitative comparisons, a parametrization specific to the O-H....O complexes is used. The vibrational energy levels of the one-dimensional ground state adiabatic potential of the model are used to make quantitative comparisons with a vast body of condensed phase data, spanning a donor-acceptor separation (R) range of about 2.4-3.0 A, i.e., from strong to weak bonds. The position of the proton and its longitudinal vibrational frequency, along with the isotope effects in both are discussed. An analysis of the secondary geometric isotope effects, using a simple extension of the two-state model, yields an improved agreement of the predicted variation with R of frequency isotope effects. The role of the bending modes in also considered: their quantum effects compete with those of the stretching mode for certain ranges of H-bond strengths. In spite of the economy in the parametrization of the model used, it offers key insights into the defining features of H-bonds, and semi-quantitatively captures several experimental trends.Comment: 12 pages, 8 figures. Notation clarified. Revised figure including the effect of bending vibrations on secondary geometric isotope effect. Final version, accepted for publication in Journal of Chemical Physic

Experimental Investigation on the Cold – Formed Steel – Concrete Composite Beam Under Flexure

Steel – concrete composite members are widely used in the construction of multistorey buildings and bridges. Composite construction takes the advantages of steel and concrete, in turn reduces the cost of construction. This paper discuss the use of galvanised steel with concrete as composite beam under flexural loading. Six specimens were tested to failure with varying number of headed stud connectors from 0 to 5. Load carrying capacity of the composite beam specimen improved by 62 % as compared to beam without shear connectors. The mode of failure of the composite beam is mainly due to failure of the shear connectors at tension zone, which leads to formation of multiple cracks on concrete portion. The analytical model was developed using finite element software ANSYS and found to obtain similar result as compared to the experimental results with minimal variation in the central deflection

Federated Conformal Predictors for Distributed Uncertainty Quantification

Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing step to already trained models. In this paper, we extend conformal prediction to the federated learning setting. The main challenge we face is data heterogeneity across the clients -- this violates the fundamental tenet of \emph{exchangeability} required for conformal prediction. We propose a weaker notion of \emph{partial exchangeability}, better suited to the FL setting, and use it to develop the Federated Conformal Prediction (FCP) framework. We show FCP enjoys rigorous theoretical guarantees and excellent empirical performance on several computer vision and medical imaging datasets. Our results demonstrate a practical approach to incorporating meaningful uncertainty quantification in distributed and heterogeneous environments. We provide code used in our experiments \url{https://github.com/clu5/federated-conformal}.Comment: 23 pages, 18 figures, accepted to International Conference on Machine Learning (ICML) 202