Sequential antibiotic therapy in the laboratory and in the patient

Laboratory experiments suggest that rapid cycling of antibiotics during the course of treatment could successfully counter resistance evolution. Drugs involving collateral sensitivity could be particularly suitable for such therapies. However, the environmental conditions in vivo differ from those in vitro. One key difference is that drugs can be switched abruptly in the laboratory, while in the patient, pharmacokinetic processes lead to changing antibiotic concentrations including periods of dose overlaps from consecutive administrations. During such overlap phases, drug–drug interactions may affect the evolutionary dynamics. To address the gap between the laboratory and potential clinical applications, we set up two models for comparison—a ‘laboratory model’ and a pharmacokinetic-pharmacodynamic ‘patient model’. The analysis shows that in the laboratory, the most rapid cycling suppresses the bacterial population always at least as well as other regimens. For patient treatment, however, a little slower cycling can sometimes be preferable if the pharmacodynamic curve is steep or if drugs interact antagonistically. When resistance is absent prior to treatment, collateral sensitivity brings no substantial benefit unless the cell division rate is low and drug cycling slow. By contrast, drug–drug interactions strongly influence the treatment efficiency of rapid regimens, demonstrating their importance for the optimal choice of drug pairs

A Report on Literature Search and Archaeological Survey in the Vicinity of Point Comfort, Calhoun County, Texas

During May 1979, archaeologists from the Center for Archaeological Research, The University of Texas at San Antonio, conducted literature research and an archaeological field survey of an area to be developed by the Formosa Plastic Company near Point Comfort, Calhoun County, Texas. The field and literature survey was done under contract between the Center for Archaeological Research (UTSA) and the Pace Company of Houston, Texas. In this report, the results of the field survey are presented by Thomas C. Kelly, Research Associate of the Center; the literature review was prepared by Herbert Uecker, Technical Staff Assistant. All project work was done under the supervision of Dr. Thomas R. Hester, Director of the Center, and Mr. Jack D. Eaton, Assistant Director

Refinement of the crystal structure of praseodymium orthoscandate, PrScO3

O3PrSc, Prima (no. 62), a = 5.780(1) Å, b = 8.025(2) Å, c = 5.608(1) Å, V= 260.1 Å3, Z = 4, R gr(F) = 0.025, wRref(F2) = 0.060, T= 298 K. © by Oldenbourg Wissenschaftsverlag, München

Crystal structure of distrontium praseodym gallium pentaoxide, Sr2PrGaO5

GaO5PrSr2, tetragonal, I4/mcm (No. 140), a = 6.8441(2) Å, c = 11.2534(4) Å, V = 527.1 Å3, Z = 4, R(P) = 0.035, wR(P) = 0.052, R(I) = 0.038, T = 295 K. © by Oldenbourg Wissenschaftsverlag

Refinement of the crystal structure of praseodymium orthoscandate, PrScO3

O3PrSc, Prima (no. 62), a = 5.780(1) Å, b = 8.025(2) Å, c = 5.608(1) Å, V= 260.1 Å3, Z = 4, R gr(F) = 0.025, wRref(F2) = 0.060, T= 298 K. © by Oldenbourg Wissenschaftsverlag, München

Crystal structure of praseodym gallate, Pr4Ga2O9

Ga2O9Pr4, monoclinic, P121/c1 (No. 14), a = 7.8256(4) Å, b = 11.0322(5) Å, c = 11.4959(7) Å, β = 109.187(3)°, V = 937.4 Å3, Z = 4, R(P) = 0.026, wR(P) = 0.034, R(I)= 0.033, T = 295 K

The Covid-19 pandemic: basic insights from basic mathematical models

Mathematical models for the spread of infectious diseases have a long history. From the start of the Covid-19 pandemic, there was a huge public interest in applying such models, since they help to understand general features of epidemic spread and support the assessment of possible mitigation measures – and their later relaxation. We describe and discuss some well-established mathematical models for epidemic spread, starting from the susceptible-infected-recovered (SIR) model and branching processes and discussing insights from network-based models. During the Covid-19 pandemic, such classical models have also been extended to include many additional aspects that affect epidemic spread, such as mobility patterns or testing possibilities. However, such complex models are increasingly difficult to assess from the outside. In a situation where their predictions can directly affect the lives of millions of people, this can become a severe problem. We argue that simple mathematical models have huge merits and can explain many of the key features of more complex models, such as the importance of heterogeneity in disease transmission. For example, basic models allow inferring whether super-spreading, where very few infected individuals cause the vast majority of secondary cases, should be the rule or the exception – with wide-ranging consequences for the possible success of mitigation measures. In addition, these basic models are simple enough to be understood and implemented without expert knowledge in theoretical epidemiology or computer science. Thus, they offer a level of transparency that can be important for a society to accept mitigation measures

Anaesthetics and cardiac preconditioning. Part I. Signalling and cytoprotective mechanisms

Cardiac preconditioning represents the most potent and consistently reproducible method of rescuing heart tissue from undergoing irreversible ischaemic damage. Major milestones regarding the elucidation of this phenomenon have been passed in the last two decades. The signalling and amplification cascades from the preconditioning stimulus, be it ischaemic or pharmacological, to the putative end‐effectors, including the mechanisms involved in cellular protection, are discussed in this review. Volatile anaesthetics and opioids effectively elicit pharmacological preconditioning. Anaesthetic‐induced preconditioning and ischaemic preconditioning share many fundamental steps, including activation of G‐protein‐coupled receptors, multiple protein kinases and ATP‐sensitive potassium channels (KATP channels). Volatile anaesthetics prime the activation of the sarcolemmal and mitochondrial KATP channels, the putative end‐effectors of preconditioning, by stimulation of adenosine receptors and subsequent activation of protein kinase C (PKC) and by increased formation of nitric oxide and free oxygen radicals. In the case of desflurane, stimulation of α‐ and β‐adrenergic receptors may also be of importance. Similarly, opioids activate δ‐ and κ‐opioid receptors, and this also leads to PKC activation. Activated PKC acts as an amplifier of the preconditioning stimulus and stabilizes, by phosphorylation, the open state of the mitochondrial KATP channel (the main end‐effector in anaesthetic preconditioning) and the sarcolemmal KATP channel. The opening of KATP channels ultimately elicits cytoprotection by decreasing cytosolic and mitochondrial Ca2+ overload. Br J Anaesth 2003; 91: 551-6

Perturbation theory for plasmonic eigenvalues

We develop a perturbative approach for calculating, within the quasistatic approximation, the shift of surface resonances in response to a deformation of a dielectric volume. Our strategy is based on the conversion of the homogeneous system for the potential which determines the plasmonic eigenvalues into an inhomogeneous system for the potential's derivative with respect to the deformation strength, and on the exploitation of the corresponding compatibility condition. The resulting general expression for the first-order shift is verified for two explicitly solvable cases, and for a realistic example of a deformed nanosphere. It can be used for scanning the huge parameter space of possible shape fluctuations with only quite small computational effort

A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, in terms of reconstruction error, perceptual quality and reconstruction speed for both 3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the method proposed is approximately twice as small, allowing to preserve anatomical structures more faithfully. Using our method, each image can be reconstructed in 23 ms, which is fast enough to enable real-time applications