Whole-body kinetics and dosimetry of L-3--123I-iodo-alpha-methyltyrosine

BACKGROUND: Desflurane anesthesia can produce cerebral metabolic depression and increase cerebral blood flow. We evaluated the effect of desflurane on brain tissue oxygen pressure (PO2), carbon dioxide pressure (PCO2) and pH during neurosurgery. METHODS: Following a craniotomy, the dura was opened and a Paratrend 7 sensor, which measures PO2, PCO2, pH and temperature, was inserted into brain tissue. In 6 control patients in group 1, anesthesia was maintained constant with 3% end-tidal desflurane over 60 min, including a 30-min stabilization period. In group 2, 9 patients were ventilated with 3% desflurane under baseline conditions. After a 30-min stabilization period, baseline tissue gases and pH were measured and end-tidal desflurane was increased to 6% and then 9% for 15-min intervals. Mean arterial pressure (MAP) was maintained with intravenous phenylephrine. RESULTS: Under baseline conditions, cardiovascular and brain tissue measures were similar between the 2 groups. Increasing end-tidal desflurane from 3% to 9% produced burst-suppression EEG in all patients and significantly increased tissue PO2 and pH and decreased PCO2. No parameters changed significantly in the control group during steady-state anesthesia. CONCLUSION: These results show that 9% desflurane can improve brain tissue metabolic status before temporary brain artery occlusion if cerebral perfusion pressure is maintained. This may be particularly important in patients with symptoms of ischemia before surgery.     

Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

Selective hydrogenation of soybean oil in the presence of copper catalysts

Many investigators associate the poor keeping properties of soybean oil with its linolenic acid content. On the other hand the high linoleic acid content is a desired property from a nutritional point of view. We have therefore developed a process for the preferential reduction of the linolenic acid content by selective hydrogenation. Conventional catalysts for the hydrogenation of fats have a rather low selectivity in this respect. When linolenic acid in soybean oil is hardened (e.g., with a nickel catalyst), most of the linoleic acid is converted into less unsaturated acids. It was found that linolenic acid is hydrogenated much more preferentially in the presence of copper catalysts than in that of nickel and other hydrogenation catalysts. At a linolenic acid content of 2%, soybean oil hardened with nickel catalyst contained about 28% linoleic acid, whereas with copper catalyst the hardened soybean oil contained 49% linoleic acid. By means of our process it is possible to manufacture a good keepable oil of, e.g., I.V. 115 and containing 1% linolenic acid and 46% linoleic acid. The storage stability of this product is comparable with that of sunflower-seed oil. A liquid phase yield of 86% is obtained after winterization at 5C for 18 hr. The high selectivity for linolenate reduction of copper catalysts must be ascribed to the copper part of the catalyst. Investigations into the structure of the catalyst indicate that the active center consists of copper metal crystallites; whether these centers are promoted by the carrier or traces of other substances is under investigation.     

Validity and inter-observer reliability of subjective hand-arm vibration assessments

Exposure to mechanical vibrations at work (e.g., due to handling powered tools) is a potential occupational risk as it may cause upper extremity complaints. However, reliable and valid assessment methods for vibration exposure at work are lacking. Measuring hand-arm vibration objectively is often difficult and expensive, while often used information provided by manufacturers lacks detail. Therefore, a subjective hand-arm vibration assessment method was tested on validity and inter-observer reliability.     

Hoare's logic and VDM

A relational framework which unifies Hoare's logic and VDM is presented. Within this framework a partial correctness version of VDM is defined. It is argued that this partial correctness version of VDM is intuitive and consistent with the original total correctness version. Furthermore it is shown how both partial and total correctness formulae and specifications can be translated from Hoare's logic into VDM and vice versa. VDM's satisfiability requirement is briefly discussed, and a similar condition for Hoare's logic is defined.:Supported by NWO/SION project 612-316-103: Fault Tolerance: Paradigms, Models, Logics, Construction.     

The Multimeter at the Nanoscale

A multi‐tip scanning tunneling microscope (STM) specifically designed for charge transport measurements at the nanoscale is described. Complementing the instrument with a versatile measurement electronics creates a powerful tool to give insight into fundamental transport properties at the nanoscale. We demonstrate the capabilities of the instrument by measuring resistance profiles along freestanding GaAs nanowires, by the acquisition of nanoscale potential maps, and by the identification of an anisotropy in the surface conductivity at a silicon surface.     

All normalized anti-monotonic overlap graph measures are bounded

In graph mining, a frequency measure for graphs is anti-monotonic if the frequency of a pattern never exceeds the frequency of a subpattern. The efficiency and correctness of most graph pattern miners relies critically on this property. We study the case where frequent subgraphs have to be found in one graph. Vanetik et al. (Data Min Knowl Disc 13(2):243?C260, 2006) already gave sufficient and necessary conditions for anti-monotonicity of graph measures depending only on the edge-overlaps between the instances of the pattern in a labeled graph. We extend these results to homomorphisms, isomorphisms and homeomorphisms on both labeled and unlabeled, directed and undirected graphs, for vertex- and edge-overlap. We show a set of reductions between the different morphisms that preserve overlap. As a secondary contribution, we prove that the popular maximum independent set measure assigns the minimal possible normalized frequency and we introduce a new measure based on the minimum clique partition that assigns the maximum possible normalized frequency. In that way, we obtain that all normalized anti-monotonic overlap graph measures are bounded from above and below. We also introduce a new measure sandwiched between the former two based on the polynomial time computable Lovász ??-function.     

Three naive Bayes approaches for discrimination-free classification

In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data.     

Improved methods for extracting frequent itemsets from interim‐support trees

Mining association rules in relational databases is a significant computational task with lots of applications. A fundamental ingredient of this task is the discovery of sets of attributes (itemsets) whose frequency in the data exceeds some threshold value. In this paper we describe two algorithms for completing the calculation of frequent sets using a tree structure for storing partial supports, called interim‐support (IS) tree. The first of our algorithms (T‐Tree‐First (TTF)) uses a novel tree pruning technique, based on the notion of (fixed‐prefix) potential inclusion, which is specially designed for trees that are implemented using only two pointers per node. This allows to implement the IS tree in a space‐efficient manner. The second algorithm (P‐Tree‐First (PTF)) explores the idea of storing the frequent itemsets in a second tree structure, called the total support tree (T‐tree); the main innovation lies in the use of multiple pointers per node, which provides rapid access to the nodes of the T‐tree and makes it possible to design a new, usually faster, method for updating them. Experimental comparison shows that these techniques result in considerable speedup for both algorithms compared with earlier approaches that also use IS trees (Principles of Data Mining and Knowledge Discovery, Proceedings of the 5th European Conference, PKDD, 2001, Freiburg, September 2001 (Lecture Notes in Artificial Intelligence, vol. 2168). Springer: Berlin, Heidelberg, 54–66; Journal of Knowledge‐Based Syst. 2000; 13 :141–149). Further comparison between the two new algorithms, shows that the PTF is generally faster on instances with a large number of frequent itemsets, provided that they are relatively short, whereas TTF is more appropriate whenever there exist few or quite long frequent itemsets; in addition, TTF behaves well on instances in which the densities of the items of the database have a high variance. Copyright © 2008 John Wiley & Sons, Ltd.