A study of the cell wall-associated proteinase of lactic streptococci : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in biochemistry at Massey University

The cell wall proteinase of Streptococcus lactis 4760 was released by incubation of milk­ grown cells in Ca++- free buffer. The effects of duration of incubation, pH and presence of Ca++ ions on the release of proteinase activity was investigated. The extent of leakage of intracellular enzymes during incubation was monitored by the appearance of lactate dehydrogenase activity in the incubation buffer. The proteinase released from the cells was partially purified by ion- exchange and gel permeation chromatography and then analysed for activity towards various milk- proteins. Only a single proteinase was evident from the purification. This enzyme was active towards - casein but showed no apparent cleavage of a 81- and K- caseins nor the whey proteins, a- lactalbumin and - lactoglobulin. The enzyme cleaved the - casein molecule within the C- terminal 49 residues, generating four main peptides containing residues 167- 175, 176- 182, 183- 193 and 194-209, and smaller amounts of peptides corresponding to the overlapping sequences 161- 166, 164- 169 and 166- 175. The four main peptides are identical to those generated by an S. lactis 763 proteinase described by Monnet et al. (1986) and by an S. cremoris HP proteinase recently described by Visser et al. (1987). No apparent specificity of enzyme action was evident. A preliminary study of the cell wall proteinase from S. cremoris SKl 1, a strain reported to produce a proteinase with a different specificity, suggested that the enzyme may hydrolyse the - casein molecule at the same sites as those cleaved by the S. lactis 4760 enzyme

Learning Comprehensible Theories from Structured Data

This thesis is concerned with the problem of learning comprehensible theories from structured data and covers primarily classification and regression learning. The basic knowledge representation language is set around a polymorphically-typed, higher-order logic. The general setup is closely related to the learning from propositionalized knowledge and learning from interpretations settings in Inductive Logic Programming. Individuals (also called instances) are represented as terms in the logic. A grammar-like construct called a predicate rewrite system is used to define features in the form of predicates that individuals may or may not satisfy. For learning, decision-tree algorithms of various kinds are adopted.¶ The scope of the thesis spans both theory and practice. ..

Stainless steel structures in fire

The initial material cost of structural stainless steel is about four times that of structural carbon steel, due largely to the expense of the alloying elements and the relatively low volume of production. Given broadly similar structural performance, additional areas of benefit need to be identified and exploited in order to establish stainless steel as a viable alternative material for construction. In addition to the familiar benefits of corrosion resistance, low maintenance, high residual value and aesthetics, one such area is fire resistance. Material properties and their response to elevated temperatures form an essential part of structural fire design. The mechanical and thermal properties of stainless steel differ from those of carbon steel due to variation in chemical composition between the materials. A comparison of these properties for austenitic stainless steel with those for structural carbon steel is presented in this thesis, and implications of the differences explored. A total of 23 column buckling tests, 6 stub column tests, 5 simply supported beams, 1 continuous beam and 14 temperature development tests have previously been conducted on stainless steel sections in fire. These tests have been replicated numerically using the non-linear finite element package ABAQUS. Following accurate replication of the tests, a series of parametric studies were performed to expand the range of available data. Based on comparisons between all available test data and the current design rules in Eurocode 3: Part 1.2, together with the findings of the numerical study, a number of revisions to the code have been proposed. They include revised values for the heat transfer coefficient and emissivity, revised buckling curve, consistent strain limits and a new approach to the treatment of cross- section classification and local buckling. These revisions have led to a more accurate determination of temperature development in structural stainless steel, and provide more efficient and more consistent treatment of buckling of stainless steel structures in fire.Open Acces

Reinforcement Learning via AIXI Approximation

This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms. We answer this hitherto open question in the affirmative, by providing the first computationally feasible approximation to the AIXI agent. To develop our approximation, we introduce a Monte Carlo Tree Search algorithm along with an agent-specific extension of the Context Tree Weighting algorithm. Empirically, we present a set of encouraging results on a number of stochastic, unknown, and partially observable domains.Comment: 8 LaTeX pages, 1 figur

Declarative programming for agent applications

This paper introduces the execution model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic programming idioms, higher-order functions, modal computation, probabilistic computation, and some theorem-proving capabilities. The need for these features is motivated and examples are given to illustrate the central ideas

Probabilities on Sentences in an Expressive Logic

Automated reasoning about uncertain knowledge has many applications. One difficulty when developing such systems is the lack of a completely satisfactory integration of logic and probability. We address this problem directly. Expressive languages like higher-order logic are ideally suited for representing and reasoning about structured knowledge. Uncertain knowledge can be modeled by using graded probabilities rather than binary truth-values. The main technical problem studied in this paper is the following: Given a set of sentences, each having some probability of being true, what probability should be ascribed to other (query) sentences? A natural wish-list, among others, is that the probability distribution (i) is consistent with the knowledge base, (ii) allows for a consistent inference procedure and in particular (iii) reduces to deductive logic in the limit of probabilities being 0 and 1, (iv) allows (Bayesian) inductive reasoning and (v) learning in the limit and in particular (vi) allows confirmation of universally quantified hypotheses/sentences. We translate this wish-list into technical requirements for a prior probability and show that probabilities satisfying all our criteria exist. We also give explicit constructions and several general characterizations of probabilities that satisfy some or all of the criteria and various (counter) examples. We also derive necessary and sufficient conditions for extending beliefs about finitely many sentences to suitable probabilities over all sentences, and in particular least dogmatic or least biased ones. We conclude with a brief outlook on how the developed theory might be used and approximated in autonomous reasoning agents. Our theory is a step towards a globally consistent and empirically satisfactory unification of probability and logic.Comment: 52 LaTeX pages, 64 definiton/theorems/etc, presented at conference Progic 2011 in New Yor

Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology

Abstract Background In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN). Results The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters. Conclusions This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.http://deepblue.lib.umich.edu/bitstream/2027.42/78257/1/1471-2105-11-505.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/2/1471-2105-11-505-S1.DOChttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/3/1471-2105-11-505.pdfPeer Reviewe