Cytokines have been implicated in the pathogenesis of the euthyroid sick syndrome. Isolated limb perfusion (ILP) with recombinant human tumor necrosis factor alpha (rTNF) and melphalan in patients with melanoma or sar...
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Cytokines have been implicated in the pathogenesis of the euthyroid sick syndrome. Isolated limb perfusion (ILP) with recombinant human tumor necrosis factor alpha (rTNF) and melphalan in patients with melanoma or sarcoma is accompanied by high systemic TNF: levels. We examined the prolonged effects (7 days) of ILP on thyroid hormone metabolism with respect to induction and recovery of the euthyroid sick syndrome in six cancer patients. After ILP, when the limb is reconnected to the systemic circulation, leakage of residual rTNF resulted in systemic peak levels at. 10 minutes postperfusion followed by a parallel increase in plasma interleukin-6 (IL-6) and cortisol, with maximum levels at 4 hours (P < .05). A rapid decrease was observed at 5 minutes for plasma triiodothyronine (T3), reverse T3 (rT3), thyroxine (T4), and thyroxine-binding globulin (TBG) (P < .05), whereas free T4 (FT4) and T3-uptake showed a sharp increase, with peak levels at 5 minutes (P < .05). T3, T4, and TBG levels remained low until 24 hours after ILP. In contrast, rT3 increased above pretreatment values to maximum levels at 24 hours (P < .05), Plasma thyrotropin (TSH) showed an initial decrease at 4 hours postperfusion (P < .05) but exceeded pretreatment values from day 1 to day 7 (by +94% +/- 43% to +155% +/- 66%, P < .05), preceding the recovery of T4 and T3 levels. T3 and rT3 returned to initial values at day 4. T4 and TBG levels recovered at day 2. T4 exceeded basal values at days 5 to 7 (P < .05). It is concluded that ILP with rTNF induces a euthyroid sick syndrome either directly or indirectly through other mediators such as IL-6 or cortisol. The recovery from this euthyroid sick syndrome is, at least in part, TSH-dependent, since the prolonged elevation of TSH values preceded and persisted during the normalization of T3 and the elevation of T4 levels. This biphasic pattern of induction of and recovery from the euthyroid sick syndrome may be a general feature of nonthyroidal disease. The
Discovery of frequent patterns has been studied in a variety of data mining settings. In its simplest form, known from association rule mining, the task is to discover all frequent itemsets, i.e., all combinations of ...
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Discovery of frequent patterns has been studied in a variety of data mining settings. In its simplest form, known from association rule mining, the task is to discover all frequent itemsets, i.e., all combinations of items that are found in a sufficient number of examples. The fundamental task of association rule and frequent set discovery has been extended in various directions, allowing more useful patterns to be discovered with special purpose algorithms. We present WARMR, a general purpose inductive logic programming algorithm that addresses frequent query discovery: a very general DATALOG formulation of the frequent pattern discovery problem. The motivation for this novel approach is twofold. First, exploratory data mining isi well supported: WARMR offers the flexibility required to experiment with standard and in particular novel settings not supported by special purpose algorithms. Also, application prototypes based on WARMR can be used as benchmarks in the comparison and evaluation of new special purpose algorithms. Second, the unified representation gives insight to the blurred picture of the frequent pattern discovery domain. Within the DATALOG formulation a number of dimensions appear that relink diverged settings. We demonstrate the frequent query approach and its use on two applications, one in alarm analysis, and one in a chemical toxicology domain.
Although top-down induction of decision trees is a very popular induction method, up till now it has mainly been used for propositional learning;relational decision tree learners are scarce. This dissertation discusse...
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Although top-down induction of decision trees is a very popular induction method, up till now it has mainly been used for propositional learning;relational decision tree learners are scarce. This dissertation discusses the application domain of decision tree learning and extends it towards the first order logic context of inductive logic programming.
This paper describes LPMEME, a new learning algorithm for inductive logic programming that uses statistical techniques to find first-order patterns. LPMEME takes as input examples in the form of logical facts and outp...
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Machine Learning from examples may be used, within Artificial Intelligence, as a way to acquire general knowledge or associate to a concrete problem solving system. inductive learning methods are typically used to acq...
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Machine Learning from examples may be used, within Artificial Intelligence, as a way to acquire general knowledge or associate to a concrete problem solving system. inductive learning methods are typically used to acquire general knowledge from examples. Lazy methods are those in which the experience is accessed, selected and used in a problem-centered way. In this paper we report important approaches to inductive learning methods such as propositional and relational learners, with an emphasis in inductive logic programming based methods, as well as to lazy methods such as instance-based and case-based reasoning. (C) 1998 Elsevier Science B.V.
Prolog program synthesis can be made more efficient by using schemata which capture similarities in previously-seen programs. Such schemata narrow the search involved in the synthesis of a new program. We define a gen...
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Prolog program synthesis can be made more efficient by using schemata which capture similarities in previously-seen programs. Such schemata narrow the search involved in the synthesis of a new program. We define a generalization operator for forming schemata from programs and a downward refinement operator for constructing programs from schemata. These operators define schema-hierarchy graphs which can be used to aid in the synthesis of new programs. Algorithms are presented for efficiently obtaining least generalizations of schemata, for adding new schemata to a schema-hierarchy graph, and for using schemata to construct new programs. (C) 1998 Elsevier Science B.V.
Learning from "structured examples" is necessary in a number of settings, including inductive logic programming. Here we analyze a simple learning problem in which examples have non-trivial structure: specif...
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Learning from "structured examples" is necessary in a number of settings, including inductive logic programming. Here we analyze a simple learning problem in which examples have non-trivial structure: specifically, a learning problem in which concepts are strings over a fixed alphabet, examples are deterministic finite automata (DFAs), and a string represents the set of all DFAs that accept it. We show that solving this "dual" DFA learning problem is hard, under cryptographic assumptions. This result implies the hardness of several other more natural learning problems, including learning the description logic CLASSIC from subconcepts, and learning arity-two "determinate" function-free Prolog clauses from ground clauses. The result also implies the hardness of two formal problems related to the area of "programming by demonstration": learning straightline programs over a fixed operator set from input-output pairs, and learning straightline programs from input-output pairs and "partial traces".
In this papers we examine the issue of learning multiple predicates from given training examples. A proposed MPL-CORE algorithm efficiently induces Horn clauses from examples and background knowledge by employing a si...
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In this papers we examine the issue of learning multiple predicates from given training examples. A proposed MPL-CORE algorithm efficiently induces Horn clauses from examples and background knowledge by employing a single predicate learning module CORE. A fast failure mechanism is also proposed which contributes learning efficiency and learnability to the algorithm. MPL-CORE employs background knowledge that can be represented in intensional (Horn clauses) or extensional (ground atoms) forms during its learning process. With the fast failure mechanism, MPL-CORE outperforms previous multiple predicate learning systems in both the computational complexity and learnability.
Cooperative Information Systems (CIS) often consist of applications that access shared resources such as databases. Since centralized systems may have a great impact on the system performance, parallel and distributio...
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ISBN:
(纸本)0818683805
Cooperative Information Systems (CIS) often consist of applications that access shared resources such as databases. Since centralized systems may have a great impact on the system performance, parallel and distribution techniques are needed for attaining scalability. Distributed databases are, then, crucial for the development of cooperative applications. However, in order to improve performance, it is very important to design information distribution properly, which is the goal of Distribution Design. Considering the various difficulties embedded in the Design of Distributed Object Oriented Databases, this work presents an algorithm to assist distribution designers in their task. The analysis algorithm indicates the most adequate fragmentation technique (vertical, horizontal or mixed) for each class in the database schema, and we propose the use of a machine learning method - inductive logic programming - to uncover some implicit issues to be considered in the distribution design, thus revising the proposed analysis algorithm.
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