Using CBR to improve the usability of numerical models

In this thesis we show that CBR systems can be constructed from numerical models, so as to improve their usability. It is shown that CBR models may be queried in a flexible manner, and that the user may formulate queries consisting of constraints over both “input” and “output” variables of the numerical model. It is also shown that the constraints may be formulated using either nominal or continuous variables. A generalization of the CBR retrieval process to include constraints over unified “input-output” space is formulated as a framework for the method. The method is illustrated with practical engineering models: the pneumatic conveyor problem and the projectile problem. Comparisons are made on usability of CBR and numerical models for specific problems. It is shown that CBR models can answer questions difficult or impossible to formulate using numerical models, and that CBR models can be faster. The thesis also addresses a latent problem with the general method, which is of importance generally. This is to do with interpolation over nominal values in unified space. A novel method is proposed for interpolation over nominal values, termed Generalised Shepard Nearest Neighbour method (GSNN). GSNN can utilise distance metrics defined on the solution space of a CBR system. The properties and advantages of GSNN are examined in the thesis. A comparison is made with other CBR retrieval methods, using several examples, including the travel domain case base. It is shown that GSNN can out-perform conventional nearest neighbour methods. It is shown that GSNN has advantages in that it can find solutions not in the case base and it can find solutions not in the retrieval set. It is also shown that the performance of GSNN can be improved further by using it in conjunction with a diversity algorithm. The merit of using GSNN as a case selection component is examined, and it is shown that it can give good results in sparse case bases. Finally the thesis concludes with a survey of numerical models where CBR construction can be useful, and where benefits can be expected

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Case base adaptation using solution-space metrics

In this paper we propose a generalisation of the k-nearest neighbour (k-NN) retrieval method based on an error function using distance metrics in the solution and problem space. It is an interpolative method which is proposed to be effective for sparse case bases. The method applies equally to nominal, continuous and mixed domains, and does not depend upon an embedding n-dimensional space. In continuous Euclidean problem domains, the method is shown to be a generalisation of the Shepard's Interpolation method. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The performance of the retrieval method is examined with reference to the Iris classification problem,and to a simulated sparse nominal value test problem. The introducion of a solution-space metric is shown to out-perform conventional nearest neighbours methods on sparse case bases

Case base reduction using solution-space metrics

In this paper we propose a case base reduction technique which uses a metric defined on the solution space. The technique utilises the Generalised Shepard Nearest Neighbour (GSNN) algorithm to estimate nominal or real valued solutions in case bases with solution space metrics. An overview of GSNN and a generalised reduction technique, which subsumes some existing decremental methods, such as the Shrink algorithm, are presented. The reduction technique is given for case bases in terms of a measure of the importance of each case to the predictive power of the case base. A trial test is performed on two case bases of different kinds, with several metrics proposed in the solution space. The tests show that GSNN can out-perform standard nearest neighbour methods on this set. Further test results show that a caseremoval order proposed based on a GSNN error function can produce a sparse case base with good predictive power

Case-based reasoning as a tool to improve the usability of numerical models

There may be advantages to be gained by combining Case-Based Reasoning (CBR) techniques with numerical models. In this paper we consider how CBR can be used as a flexible query engine to improve the usability of numerical models. Particularly they can help to solve inverse and mixed problems, and to solve constraint problems. We discuss this idea with reference to the illustrative example of a pneumatic conveyor. We describe a model of the problem of particle degradation in such a conveyor, and the problems faced by design engineers. The solution of these problems requires a system that allows iterative sharing of control between user, CBR system, and numerical model. This multi-initiative interaction is illustrated for the pneumatic conveyor by means of Unified Modeling Language (UML) collaboration and sequence diagrams. We show approaches to the solution of these problems via a CBR tool

A case based model to assist in the design process in the manufacture of furniture products

This paper describes progress on a project to utilise case based reasoning methods in the design and manufacture of furniture products. The novel feature of this research is that cases are represented as structures in a relational database of products, components and materials. The paper proposes a method for extending the usual "weighted sum" over attribute similarities for a ·single table to encompass relational structures over several tables. The capabilities of the system are discussed, particularly with respect to differing user objectives, such as cost estimation, CAD, cutting scheme re-use, and initial design. It is shown that specification of a target case as a relational structure combined with suitable weights can fulfil several user functions. However, it is also shown that some user functions cannot satisfactorily be specified via a single target case. For these functions it is proposed to allow the specification of a set of target cases. A derived similarity measure between individuals and sets of cases is proposed

CBE-conveyor: a case-based reasoning system to assist engineers in designing conveyor systems

In this paper, we address the use of CBR in collaboration with numerical engineering models. This collaborative combination has a particular application in engineering domains where numerical models are used. We term this domain “Case Based Engineering” (CBE), and present the general architecture of a CBE system. We define and discuss the general characteristics of CBE and the special problems which arise. These are: the handling of engineering constraints of both continuous and nominal kind; interpolation over both continuous and nominal variables, and conformability for interpolation. In order to illustrate the utility of the method proposed, and to provide practical examples of the general theory, the paper describes a practical application of the CBE architecture, known as CBE-CONVEYOR, which has been implemented by the authors.Pneumatic conveying is an important transportation technology in the solid bulks conveying industry. One of the major industry concerns is the attrition of powders and granules during pneumatic conveying. To minimize the fraction of particles during pneumatic conveying, engineers want to know what design parameters they should use in building a conveyor system. To do this, engineers often run simulations in a repetitive manner to find appropriate input parameters. CBE-Conveyor is shown to speed up conventional methods for searching for solutions, and to solve problems directly that would otherwise require considerable intervention from the engineer

Case based adaptation using interpolation over nominal values

In this paper we propose a method for interpolation over a set of retrieved cases in the adaptation phase of the case-based reasoning cycle. The method has two advantages over traditional systems: the first is that it can predict “new” instances, not yet present in the case base; the second is that it can predict solutions not present in the retrieval set. The method is a generalisation of Shepard’s Interpolation method, formulated as the minimisation of an error function defined in terms of distance metrics in the solution and problem spaces. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The method is illustrated in the paper with reference to the Irises classification problem. It is evaluated with reference to a simulated nominal value test problem, and to a benchmark case base from the travel domain. The algorithm is shown to out-perform conventional nearest neighbour methods on these problems. Finally, GSNN is shown to improve in efficiency when used in conjunction with a diverse retrieval algorithm