Correlated local distortions of the TlO layers in Tl2_2Ba2_2CuOy_{y}: An x-ray absorption study

We have used the XAFS (x-ray-absorption fine structure) technique to investigate the local structure about the Cu, Ba, and Tl atoms in orthorhombic Tl-2201 with a superconducting transition temperature T$_c$=60 K. Our results clearly show that the O(1), O(2), Cu, and Ba atoms are at their ideal sites as given by the diffraction measurements, while the Tl and O(3) atoms are more disordered than suggested by the average crystal structure. The Tl-Tl distance at 3.5 \AA{ } between the TlO layers does not change, but the Tl-Tl distance at 3.9 \AA{ } within the TlO layer is not observed and the Tl-Ba and Ba-Tl peaks are very broad. The shorter Tl-O(3) distance in the TlO layer is about 2.33 \AA, significantly shorter than the distance calculated with both the Tl and O(3) atoms at their ideal $4e$ sites ( $x=y=$0 or $\frac{1}{2}$). A model based on these results shows that the Tl atom is displaced along the directions from its ideal site by about 0.11 \AA; the displacements of neighboring Tl atoms are correlated. The O(3) atom is shifted from the $4e$ site by about 0.53 \AA{ } roughly along the directions. A comparison of the Tl L$_{III}$-edge XAFS spectra from three samples, with T$_c$=60 K, 76 K, and 89 K, shows that the O environment around the Tl atom is sensitive to T$_c$ while the Tl local displacement is insensitive to T$_c$ and the structural symmetry. These conclusions are compared with other experimental results and the implications for charge transfer and superconductivity are discussed. This paper has been submitted to Phys. Rev. B.Comment: 20 pages plus 14 ps figures, REVTEX 3.

Antimicrobials: a global alliance for optimizing their rational use in intra-abdominal infections (AGORA)

Intra-abdominal infections (IAI) are an important cause of morbidity and are frequently associated with poor prognosis, particularly in high-risk patients. The cornerstones in the management of complicated IAIs are timely effective source control with appropriate antimicrobial therapy. Empiric antimicrobial therapy is important in the management of intra-abdominal infections and must be broad enough to cover all likely organisms because inappropriate initial antimicrobial therapy is associated with poor patient outcomes and the development of bacterial resistance. The overuse of antimicrobials is widely accepted as a major driver of some emerging infections (such as C. difficile), the selection of resistant pathogens in individual patients, and for the continued development of antimicrobial resistance globally. The growing emergence of multi-drug resistant organisms and the limited development of new agents available to counteract them have caused an impending crisis with alarming implications, especially with regards to Gram-negative bacteria. An international task force from 79 different countries has joined this project by sharing a document on the rational use of antimicrobials for patients with IAIs. The project has been termed AGORA (Antimicrobials: A Global Alliance for Optimizing their Rational Use in Intra-Abdominal Infections). The authors hope that AGORA, involving many of the world's leading experts, can actively raise awareness in health workers and can improve prescribing behavior in treating IAIs

Distribution-Based Categorization of Classifier Transfer Learning

Transfer Learning (TL) aims to transfer knowledge acquired in one problem, the source problem, onto another problem, the target problem, dispensing with the bottom-up construction of the target model. Due to its relevance, TL has gained significant interest in the Machine Learning community since it paves the way to devise intelligent learning models that can easily be tailored to many different applications. As it is natural in a fast evolving area, a wide variety of TL methods, settings and nomenclature have been proposed so far. However, a wide range of works have been reporting different names for the same concepts. This concept and terminology mixture contribute however to obscure the TL field, hindering its proper consideration. In this paper we present a review of the literature on the majority of classification TL methods, and also a distribution-based categorization of TL with a common nomenclature suitable to classification problems. Under this perspective three main TL categories are presented, discussed and illustrated with examples

Fast and accurate simulations of transmission-line metamaterials using transmission-matrix method

Recently, two-dimensional (2D) periodically L and C loaded transmission-line (TL) networks have been applied to represent metamaterials. The commercial Agilent's Advanced Design System (ADS) is a commonly-used tool to simulate the TL metamaterials. However, it takes a lot of time to set up the TL network and perform numerical simulations using ADS, making the metamaterial analysis inefficient, especially for large-scale TL networks. In this paper, we propose transmission-matrix method (TMM) to simulate and analyze the TL-network metamaterials efficiently. Compared to the ADS commercial software, TMM provides nearly the same simulation results for the same networks. However, the model-process and simulation time has been greatly reduced. The proposed TMM can serve as an efficient tool to study the TL-network metamaterials.Comment: 15 pages, 13 figure

Telomere length as a predictor of emotional processing in the brain

Shorter telomere length (TL) has been associated with the development of mood disorders as well as abnormalities in brain morphology. However, so far, no studies have considered the role TL may have on brain function during tasks relevant to mood disorders. In this study, we examine the relationship between TL and functional brain activation and connectivity, while participants (n = 112) perform a functional magnetic resonance imaging (fMRI) facial affect recognition task. Additionally, because variation in TL has a substantial genetic component we calculated polygenic risk scores for TL to test if they predict face-related functional brain activation. First, our results showed that TL was positively associated with increased activation in the amygdala and cuneus, as well as increased connectivity from posterior regions of the face network to the ventral prefrontal cortex. Second, polygenic risk scores for TL show a positive association with medial prefrontal cortex activation. The data support the view that TL and genetic loading for shorter telomeres, influence the function of brain regions known to be involved in emotional processing

Brain-mediated Transfer Learning of Convolutional Neural Networks

The human brain can effectively learn a new task from a small number of samples, which indicate that the brain can transfer its prior knowledge to solve tasks in different domains. This function is analogous to transfer learning (TL) in the field of machine learning. TL uses a well-trained feature space in a specific task domain to improve performance in new tasks with insufficient training data. TL with rich feature representations, such as features of convolutional neural networks (CNNs), shows high generalization ability across different task domains. However, such TL is still insufficient in making machine learning attain generalization ability comparable to that of the human brain. To examine if the internal representation of the brain could be used to achieve more efficient TL, we introduce a method for TL mediated by human brains. Our method transforms feature representations of audiovisual inputs in CNNs into those in activation patterns of individual brains via their association learned ahead using measured brain responses. Then, to estimate labels reflecting human cognition and behavior induced by the audiovisual inputs, the transformed representations are used for TL. We demonstrate that our brain-mediated TL (BTL) shows higher performance in the label estimation than the standard TL. In addition, we illustrate that the estimations mediated by different brains vary from brain to brain, and the variability reflects the individual variability in perception. Thus, our BTL provides a framework to improve the generalization ability of machine-learning feature representations and enable machine learning to estimate human-like cognition and behavior, including individual variability