Studies of nitrogen metabolism in soils with particular reference to the effects of certain trace elements

Imperial Users onl

Does testing enhance learning in continuing medical education?

Background: There has been growing interest in using theory-driven research to develop and evaluate continuing medical education (CME) activities. Within health professions education, testing has been shown to promote learning in a variety of different contexts, an effect referred to as test-enhanced learning (TEL). However, the extent to which TEL generalizes to CME remains unclear. The purpose of this study was to investigate whether physicians who received two intervening tests following a CME event would experience a TEL effect relative to physicians who received additional study material to review without testing. Methods: Forty-nine physicians were recruited during a local CME activity. Physicians were randomized to either a) the test group (n=26), where participants completed two 20 multiple-choice question (MCQ) quizzes related to the lecture content or b) the study group (n=23), where participants studied the same information without testing. Testing and studying occurred independently during the CME activity, and then four weeks later online. At eight weeks, participants completed a final 20-item MCQ online test. A between-subjects t-test was used to compare performance on the final test as a function of the initial educational activity (test group vs. study group).Results: Performance on the final MCQ test was equivalent for both test (Mean (SD): 75% (9.9)) and study-only (77% (7.3)) conditions (t(47) = 0.94, p=0.35). Conclusion: The null findings in the present study are contrary to previous findings demonstrating TEL among novice learner populations. The lack of TEL highlights several programmatic considerations that should be factored in before implementing TEL as a part of CME

Automated Knowledge Discovery using Neural Networks

The natural world is known to consistently abide by scientific laws that can be expressed concisely in mathematical terms, including differential equations. To understand the patterns that define these scientific laws, it is necessary to discover and solve these mathematical problems after making observations and collecting data on natural phenomena. While artificial neural networks are powerful black-box tools for automating tasks related to intelligence, the solutions we seek are related to the concise and interpretable form of symbolic mathematics. In this work, we focus on the idea of a symbolic function learner, or SFL. A symbolic function learner can be any algorithm that is able to produce a symbolic mathematical expression that aims to optimize a given objective function. By choosing different objective functions, the SFL can be tuned to handle different learning tasks. We present a model for an SFL that is based on neural networks and can be trained using deep learning. We then use this SFL to approach the computational task of automating discovery of scientific knowledge in three ways. We first apply our symbolic function learner as a tool for symbolic regression, a curve-fitting problem that has traditionally been approached using genetic evolution algorithms. We show that our SFL performs competitively in comparison to genetic algorithms and neural network regressors on a sample collection of regression instances. We also reframe the problem of learning differential equations as a task in symbolic regression, and use our SFL to rediscover some equations from classical physics from data. We next present a machine-learning based method for solving differential equations symbolically. When neural networks are used to solve differential equations, they usually produce solutions in the form of black-box functions that are not directly mathematically interpretable. We introduce a method for generating symbolic expressions to solve differential equations while leveraging deep learning training methods. Unlike existing methods, our system does not require learning a language model over symbolic mathematics, making it scalable, compact, and easily adaptable for a variety of tasks and configurations. The system is designed to always return a valid symbolic formula, generating a useful approximation when an exact analytic solution to a differential equation is not or cannot be found. We demonstrate through examples the way our method can be applied on a number of differential equations that are rooted in the natural sciences, often obtaining symbolic approximations that are useful or insightful. Furthermore, we show how the system can be effortlessly generalized to find symbolic solutions to other mathematical tasks, including integration and functional equations. We then introduce a novel method for discovering implicit relationships between variables in structured datasets in an unsupervised way. Rather than explicitly designating a causal relationship between input and output variables, our method finds mathematical relationships between variables without treating any variable as distinguished from any other. As a result, properties about the data itself can be discovered, rather than rules for predicting one variable from the others. We showcase examples of our method in the domain of geometry, demonstrating how we can re-discover famous geometric identities automatically from artificially generated data. In total, this thesis aims to strengthen the connection between neural networks and problems in symbolic mathematics. Our proposed SFL is the main tool that we show can be applied to a variety of tasks, including but not limited to symbolic regression. We show how using this approach to symbolic function learning paves the way for future developments in automated scientific knowledge discovery

Spectrum of intracranial pathology: Tumors versus infections at a tertiary care hospital

Introduction: Aga Khan University Hospital Neurosurgery Department has evolved into a high-volume centre for treatment of neurosurgical diseases. We aimed to compare the relative outcomes of intracranial tumors and CNS infections seen at our facility.Methods: Hospital records of patients admitted under the neurosurgery service between 1994-2003 were evaluated. Cases with a principal diagnosis of an intracranial lesion were identified for further study. Demographic, clinical,and surgical variables were extracted from the medical record. Length of hospital stay, ICU utilization, and in-hospital mortality were the primary outcome indicators. Data were analyzed through descriptive statistics and comparisons of meansand proportions. Results: The mortality rate was 8.7% for intracranial tumors and 18.8% for intracranial infections. Average age for tumor patients was 39.4 years and 26.5 years for patients with infections. Male predominance was seen in the tumor group (55%; p=0.02) and marginally in the infection group (51.6%; p=0.52). Mortality, length of stay and ICU utilization did not decrease significantly in either group over the ten-year period of our study. In both groups, electively admitted patients were associated with better outcomes as compared to emergent admissions. Conclusion: There is a need for better awareness and education among referring physicians to be on the lookout for patients requiring early neurosurgical referral. Careful selection of patients for surgical intervention should be practiced to ensure low mortality rates and more meaningful outcome

Equity style timing : a multi-style rotation model for the Russell large-cap and small-cap growth and value style indexes

In their search for "excess returns", investors have always considered timing strategies to be potential value-enhancement tools. While transaction costs in the past have often rendered such strategies unprofitable, we can expect the ongoing decline in these costs to be accompanied by a proliferation of timing strategies by investors. Therefore, being able to time the market accurately has significant implications for researchers and practitioners alike. In this paper, we develop a timing model based on macroeconomic and fundamental public information using Frank Russell large-cap and small-cap style indexes. Despite the fact that there exists an extensive literature on equity style timing, our study differs from other studies in that it attempts to time four different markets segments simultaneously and use a multinomial logit approach as opposed to the more common binary logit approach. The results show that the terminal wealth for the portfolio generated by our model is more than two times larger than the terminal wealth of the best performing buy-and-hold portfolio, suggesting that significant opportunities for "excess returns" can be exploited by implementing a multi-style rotation strategy that employs the investment recommendations of our model. Additional finding shows that the profitability of pursuing such strategy remains in the presence of reasonable levels of transaction costs