Perfusion

The value of perfusion is reasonably well established yet the field is still developing and the ultimate applications and potentialities of perfusion techniques arc as yet undefined. It is a subject on which, as I am sharply aware, my knowledge is very incomplete, and I will discuss, in the main, work being carried out in my department in Glasgow Royal Infirmary and the University of Glasgow

A comprehensive profile of the sociodemographic, psychosocial and health characteristics of Ontario home care clients with dementia

Abstract Introduction: This study provides a comprehensive summary of the sociodemographic, psychosocial and health characteristics of a large population-based cohort of Ontario home care clients (aged 50 years and over) with dementia and examines the variation in these characteristics in those with co-existing neurological conditions

Genetic Code Mutations: The Breaking of a Three Billion Year Invariance

The genetic code has been unchanging for some three billion years in its canonical ensemble of encoded amino acids, as indicated by the universal adoption of this ensemble by all known organisms. Code mutations beginning with the encoding of 4-fluoro-Trp by Bacillus subtilis, initially replacing and eventually displacing Trp from the ensemble, first revealed the intrinsic mutability of the code. This has since been confirmed by a spectrum of other experimental code alterations in both prokaryotes and eukaryotes. To shed light on the experimental conversion of a rigidly invariant code to a mutating code, the present study examined code mutations determining the propagation of Bacillus subtilis on Trp and 4-, 5- and 6-fluoro-tryptophans. The results obtained with the mutants with respect to cross-inhibitions between the different indole amino acids, and the growth effects of individual nutrient withdrawals rendering essential their biosynthetic pathways, suggested that oligogenic barriers comprising sensitive proteins which malfunction with amino acid analogues provide effective mechanisms for preserving the invariance of the code through immemorial time, and mutations of these barriers open up the code to continuous change

A Scoping Review of Frailty and Acute Care in Middle-Aged and Older Individuals with Recommendations for Future Research

There is general agreement that frailty is a state of heightened vulnerability to stressors arising from impairments in multiple systems leading to declines in homeostatic reserve and resiliency, but unresolved issues persist about its detection, underlying pathophysiology, and relationship with aging, disability, and multimorbidity. A particularly challenging area is the relationship between frailty and hospitalization. Based on the deliberations of a 2014 Canadian expert consultation meeting and a scoping review of the relevant literature between 2005 and 2015, this discussion paper presents a review of the current state of knowledge on frailty in the acute care setting, including its prevalence and ability to both predict the occurrence and outcomes of hospitalization. The examination of the available evidence highlighted a number of specific clinical and research topics requiring additional study. We conclude with a series of consensus recommendations regarding future research priorities in this important area

Nursing Home Prices and Quality of Care - Evidence from Administrative Data

There is widespread concern about the quality of care in nursing homes. Based on administrative data of a large health insurance fund, we investigate whether nursing home prices affect relevant quality of care indicators at the resident level. Our results indicate a significantly negative price effect on inappropriate and psychotropic medication. In contrast, we find no evidence for fewer painful physical sufferings for residents of nursing homes with higher prices.Die in den letzten Jahren ins Blickfeld der Öffentlichkeit getretenen Probleme in Pflegeheimen führten zu dem vielfach geäußerten Verdacht der medikamentösen Ruhigstellung betreuungsbedürftiger Menschen in der stationären Pflege. Mit der vorliegenden Untersuchung soll ein Beitrag zum Abbau der derzeit noch bestehenden Wissensdefizite über den Zusammenhang zwischen dem Preis und der Qualität der stationären Pflegeleistungen geleistet werden. Auf Basis von Routinedaten der Techniker Krankenkasse werden hierzu neben der Inzidenz an Verletzungen sowie weiteren gesundheitlichen Beeinträchtigungen aufgrund äußerer Umstände die verordnete Dosis potenziell inadäquater Medikamente für ältere Menschen sowie weiterer Arzneistoffklassen (u.a. Psycholeptika) als Indikatoren der Ergebnisqualität herangezogen. Die Ergebnisse der Untersuchung zeigen einen negativen Effekt der Heimpreise auf Verschreibungen potenziell inadäquater und psychotropischer Medikamente

Data and Computation Efficient Meta-Learning

In order to make predictions with high accuracy, conventional deep learning systems require large training datasets consisting of thousands or millions of examples and long training times measured in hours or days, consuming high levels of electricity with a negative impact on our environment. It is desirable to have have machine learning systems that can emulate human behavior such that they can quickly learn new concepts from only a few examples. This is especially true if we need to quickly customize or personalize machine learning models to specific scenarios where it would be impractical to acquire a large amount of training data and where a mobile device is the means for computation. We define a data efficient machine learning system to be one that can learn a new concept from only a few examples (or shots) and a computation efficient machine learning system to be one that can learn a new concept rapidly without retraining on an everyday computing device such as a smart phone. In this work, we design, develop, analyze, and extend the theory of machine learning systems that are both data efficient and computation efficient. We present systems that are trained using multiple tasks such that it "learns how to learn" to solve new tasks from only a few examples. These systems can efficiently solve new, unseen tasks drawn from a broad range of data distributions, in both the low and high data regimes, without the need for costly retraining. Adapting to a new task requires only a forward pass of the example task data through the trained network making the learning of new tasks possible on mobile devices. In particular, we focus on few-shot image classification systems, i.e. machine learning systems that can distinguish between numerous classes of objects depicted in digital images given only a few examples of each class of object to learn from. To accomplish this, we first develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. We then introduce Versa, an instance of the framework employing a fast, flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of training examples, and outputs a distribution over task-specific parameters in a single forward pass of the network. We evaluate Versa on benchmark datasets, where at the time, the method achieved state-of-the-art results when compared to meta-learning approaches using similar training regimes and feature extractor capacity. Next, we build on Versa and add a second amortized network to adapt key parameters in the feature extractor to the current task. To accomplish this, we introduce CNAPs, a conditional neural process based approach to multi-task classification. We demonstrate that, at the time, CNAPs achieved state-of-the-art results on the challenging Meta-Dataset benchmark indicating high-quality transfer-learning. Timing experiments reveal that CNAPs is computationally efficient when adapting to an unseen task as it does not involve gradient back propagation computations. We show that trained models are immediately deployable to continual learning and active learning where they can outperform existing approaches that do not leverage transfer learning. Finally, we investigate the effects of different methods of batch normalization on meta-learning systems. Batch normalization has become an essential component of deep learning systems as it significantly accelerates the training of neural networks by allowing the use of higher learning rates and decreasing the sensitivity to network initialization. We show that the hierarchical nature of the meta-learning setting presents several challenges that can render conventional batch normalization ineffective. We evaluate a range of approaches to batch normalization for few-shot learning scenarios, and develop a novel approach that we call TaskNorm. Experiments demonstrate that the choice of batch normalization has a dramatic effect on both classification accuracy and training time for both gradient based- and gradient-free meta-learning approaches and that TaskNorm consistently improves performance