Rensch's rule in large herbivorous mammals derived from metabolic scaling

Rensch’s rule, which states that the magnitude of sexual size dimorphism tends to increase with increasing body size, has evolved independently in three lineages of large herbivorous mammals: bovids (antelopes), cervids (deer), and macropodids (kangaroos). This pattern can be explained by a model that combines allometry,life-history theory, and energetics. The key features are thatfemale group size increases with increasing body size and that males have evolved under sexual selection to grow large enough to control these groups of females. The model predicts relationships among body size and female group size, male and female age at first breeding,death and growth rates, and energy allocation of males to produce body mass and weapons. Model predictions are well supported by data for these megaherbivores. The model suggests hypotheses for why some other sexually dimorphic taxa, such as primates and pinnipeds(seals and sea lions), do or do not conform to Rensh’s rule.

Combination of moderate vacuum frying with high vacuum drainage: relationship between process conditions and oil uptake

This paper explores the possibility of combining moderate vacuum frying followed by post-frying high vacuum application during the oil drainage stage, with the aim to reduce oil content in potato chips. Potato slices were initially vacuum fried under two operating conditions (140 °C, 20 kPa and 162 °C, 50.67 kPa) until the moisture content reached 10 and 15 % (wet basis), prior to holding the samples in the head space under high vacuum level (1.33 kPa). This two-stage process was found to lower significantly the amount of oil taken up by potato chips by an amount as high as 48 %, compared to drainage at the same pressure as the frying pressure. Reducing the pressure value to 1.33 kPa reduced the water saturation temperature (11 °C), causing the product to continuously lose moisture during the course of drainage. Continuous release of water vapour prevented the occluded surface oil from penetrating into the product structure and released it from the surface of the product. When frying and drainage occurred at the same pressure, the temperature of the product fell below the water saturation temperature soon after it was lifted out of the oil, which resulted in the oil getting sucked into the product. Thus, lowering the pressure after frying to a value well below the frying pressure is a promising method to lower oil uptake by the product.

Wernicke’s aphasia reflects a combination of acoustic-phonological and semantic control deficits: A case-series comparison of Wernicke’s aphasia, semantic dementia and semantic aphasia

Wernicke’s aphasia (WA) is the classical neurological model of comprehension impairment and, as a result, the posterior temporal lobe is assumed to be critical to semantic cognition. This conclusion is potentially confused by (a) the existence of patient groups with semantic impairment following damage to other brain regions (semantic dementia and semantic aphasia) and (b) an ongoing debate about the underlying causes of comprehension impairment in WA. By directly comparing these three patient groups for the first time, we demonstrate that the comprehension impairment in Wernicke’s aphasia is best accounted for by dual deficits in acoustic-phonological analysis (associated with pSTG) and semantic cognition (associated with pMTG and angular gyrus). The WA group were impaired on both nonverbal and verbal comprehension assessments consistent with a generalised semantic impairment. This semantic deficit was most similar in nature to that of the semantic aphasia group suggestive of a disruption to semantic control processes. In addition, only the WA group showed a strong effect of input modality on comprehension, with accuracy decreasing considerably as acoustic-phonological requirements increased. These results deviate from traditional accounts which emphasise a single impairment and, instead, implicate two deficits underlying the comprehension disorder in WA.

Isolation from a sheep of an attaching and effacing Escherichia coli O115:H- with a novel combination of virulence factors

Attaching and effacing (AE) lesions were observed in the caecum, proximal colon and rectum of one of four lambs experimentally inoculated at 6 weeks. of age with Escherichia coli O157:H7. However, the attached bacteria did not immunostain with O157-specific antiserum. Subsequent bacteriological analysis of samples from this animal yielded two E. coli O115:H- strains, one from the colon (CO) and one from the rectum (RC), and those bacteria forming the AE lesions were shown to be of the O115 serogroup by immunostaining. The O115:H(-)isolates formed microcolonies and attaching and effacing lesions, as demonstrated by the fluorescence actin staining test, on HEp-2 tissue culture cells. Both isolates were confirmed by PCR to encode the epsilon (epsilon) subtype of intimin. Supernates of both O115:H- isolates induced cytopathic effects on Vero cell monolayers, and PCR analysis verified that both isolates encoded EAST1, CNF1 and CNF2 toxins but not Shiga-like toxins. Both isolates harboured similar sized plasmids but-PCR analysis indicated that only one of the O115:H- isolates (CO) possessed the plasmid-associated virulence determinants ehxA and etpD. Neither strain possessed the espP, katP or bfpA plasmid-associated virulence determinants. These E. coli O115:H- strains exhibited a novel combination of virulence determinants and are the first isolates found to possess both CNF1 and CNF2.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

Parallel rule induction with information theoretic pre-pruning

In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Towards a computationally efficient approach to modular classification rule induction

Induction of classification rules is one of the most important technologies in data mining. Most of the work in this field has concentrated on the Top Down Induction of Decision Trees (TDIDT) approach. However, alternative approaches have been developed such as the Prism algorithm for inducing modular rules. Prism often produces qualitatively better rules than TDIDT but suffers from higher computational requirements. We investigate approaches that have been developed to minimize the computational requirements of TDIDT, in order to find analogous approaches that could reduce the computational requirements of Prism.

Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

Local orientational disorder in peptide fibrils probed by a combination of residue-specific 13C-18O labelling, polarised infrared spectroscopy and molecular combing

A novel combination of site-specific isotope labelling, polarised infrared spectroscopy and molecular combing reveal local orientational ordering in the fibril-forming peptide YTIAALLSPYSGGRADS. Use of 13C-18O labelled alanine residues demonstrates that the Nterminal end of the peptide is incorporated into the cross-beta structure, while the C-terminal end shows orientational disorder

An unusual axial–axial combination of alternating cyclic rings in the chair conformations of hexameric water and chlorine-water clusters

An alternating hexameric water (H2O)(6) cluster and a chlorine-water cluster [Cl-2(H2O)(4)](2-) in the chair forms combine axially to each other to form a 1D chain [{Cl-2(H2O)(6)}(2-)](n) in complex [FeL2]Cl center dot(H2O)(3) (L=2-[(2-methylaminoethylimino)-methyl]-phenol)]. The water molecules display extensive H-bonding interactions with monomeric iron-organic units to form a hydrogen-bonded 2D supramolecular assembly.

Janus PEG-based dendrimers for use in combination therapy: controlled multi-drug loading and sequential release

The increasing use of drug combinations to treat disease states, such as cancer, calls for improved delivery systems that are able to deliver multiple agents. Herein, we report a series of novel Janus dendrimers with potential for use in combination therapy. Different generations (first and second) of PEG-based dendrons containing two different “model drugs”, benzyl alcohol (BA) and 3-phenylpropionic acid (PPA), were synthesized. BA and PPA were attached via two different linkers (carbonate and ester, respectively) to promote differential drug release. The four dendrons were coupled together via (3 + 2) cycloaddition chemistries to afford four Janus dendrimers, which contained varying amounts and different ratios of BA and PPA, namely, (BA)2-G1-G1-(PPA)2, (BA)4-G2-G1-(PPA)2, (BA)2-G1-G2-(PPA)4, and (BA)4-G2-G2-(PPA)4. Release studies in plasma showed that the dendrimers provided sequential release of the two model drugs, with BA being released faster than PPA from all of the dendrons. The different dendrimers allowed delivery of increasing amounts (0.15–0.30 mM) and in exact molecular ratios (1:2; 2:1; 1:2; 2:2) of the two model drug compounds. The dendrimers were noncytotoxic (100% viability at 1 mg/mL) toward human umbilical vein endothelial cells (HUVEC) and nontoxic toward red blood cells, as confirmed by hemolysis studies. These studies demonstrate that these Janus PEG-based dendrimers offer great potential for the delivery of drugs via combination therapy.