Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

A tree augmented classifier based on Extreme Imprecise Dirichlet Model

We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

Automatically Annotating Structured Web Data Using a SVM-Based Multiclass Classifier

In this paper, we propose a new learning approach to Web data annotation, where a support vector machine-based multiclass classifier is trained to assign labels to data items. For data record extraction, a data section re-segmentation algorithm based on visual and content features is introduced to improve the performance of Web data record extraction. We have implemented the proposed approach and tested it with a large set of Web query result pages in different domains. Our experimental results show that our proposed approach is highly effective and efficient.

Malware detection: program run length against detection rate

N-gram analysis is an approach that investigates the structure of a program using bytes, characters or text strings. This research uses dynamic analysis to investigate malware detection using a classification approach based on N-gram analysis. A key issue with dynamic analysis is the length of time a program has to be run to ensure a correct classification. The motivation for this research is to find the optimum subset of operational codes (opcodes) that make the best indicators of malware and to determine how long a program has to be monitored to ensure an accurate support vector machine (SVM) classification of benign and malicious software. The experiments within this study represent programs as opcode density histograms gained through dynamic analysis for different program run periods. A SVM is used as the program classifier to determine the ability of different program run lengths to correctly determine the presence of malicious software. The findings show that malware can be detected with different program run lengths using a small number of opcodes

N-gram density based malware detection

N-gram analysis is an approach that investigates the structure of a program using bytes, characters or text strings. This research uses dynamic analysis to investigate malware detection using a classification approach based on N-gram analysis. The motivation for this research is to find a subset of Ngram features that makes a robust indicator of malware. The experiments within this paper represent programs as N-gram density histograms, gained through dynamic analysis. A Support Vector Machine (SVM) is used as the program classifier to determine the ability of N-grams to correctly determine the presence of malicious software. The preliminary findings show that an N-gram size N=3 and N=4 present the best avenues for further analysis.

The Ability of Secretory Leukocyte Protease Inhibitor to Inhibit Apoptosis in Monocytes Is Independent of Its Antiprotease Activity

Secretory Leukocyte Protease Inhibitor (SLPI) is a serine protease inhibitor produced by epithelial and myeloid cells with anti-inflammatory properties. Research has shown that SLPI exerts its anti-inflammatory activity by directly binding to NF-κB DNA binding sites and, in so doing, prevents binding and subsequent transcription of proinflammatory gene expression. In the current study, we demonstrate that SLPI can inhibit TNF-α-induced apoptosis in U937 cells and peripheral blood monocytes. Specifically, SLPI inhibits TNF-α-induced caspase-3 activation and DNA degradation associated with apoptosis. We go on to show that this ability of SLPI to inhibit apoptosis is not dependent on its antiprotease activity as antiprotease deficient variants of SLPI can also inhibit TNF-α-induced apoptosis. This reduction in monocyte apoptosis may preserve monocyte function during inflammation resolution and promote infection clearance at mucosal sites.

Aggressiveness as a component of fighting ability in pigs using a game-theoretical framework

Understanding animal contests has benefited greatly from employing the concept of fighting ability, termed resource-holding potential (RHP), with body size/weight typically used as a proxy. However, victory does not always go to the larger/heavier contestant and the existing RHP approach thereby fails to accurately predict contest outcome. Aggressiveness, typically studied as a personality trait, might explain part of this discrepancy. We investigated whether aggressiveness forms a component of RHP, examining effects on contest outcome, duration and phases, plus physiological measures of costs (lactate and glucose). Furthermore, using the correct theoretical framework, we provide the first study to investigate whether individuals gather and use information on aggressiveness as part of an assessment strategy. Pigs, Sus scrofa, were assessed for aggressiveness in resident-intruder tests whereby attack latency reflects aggressiveness. Contests were then staged between size-matched animals diverging in aggressiveness. Individuals with a short attack latency in the resident-intruder test almost always initiated the first bite and fight in the subsequent contest. However, aggressiveness had no direct effect on contest outcome, whereas bite initiation did lead to winning in contests without an escalated fight. This indirect effect suggests that aggressiveness is not a component of RHP, but rather reflects a signal of intent. Winner and loser aggressiveness did not affect contest duration or its separate phases, suggesting aggressiveness is not part of an assessment strategy. A greater asymmetry in aggressiveness prolonged contest duration and the duration of displaying, which is in a direction contrary to assessment models based on morphological traits. Blood lactate and glucose increased with contest duration and peaked during escalated fights, highlighting the utility of physiological measures as proxies for fight cost. Integrating personality traits into the study of contest behaviour, as illustrated here, will enhance our understanding of the subtleties of agonistic interactions.

The effect of the 'Gait keeper' mutation in the DMRT3 gene on gaiting ability in Icelandic horses

A nonsense mutation in DMRT3 ('Gait keeper' mutation) has a predominant effect on gaiting ability in horses, being permissive for the ability to perform lateral gaits and having a favourable effect on speed capacity in trot. The DMRT3 mutant allele (A) has been found in high frequency in gaited breeds and breeds bred for harness racing, while other horse breeds were homozygous for the wild-type allele (C). The aim of this study was to evaluate further the effect of the DMRT3 nonsense mutation on the gait quality and speed capacity in the multigaited Icelandic horse and demonstrate how the frequencies of the A- and C- alleles have changed in the Icelandic horse population in recent decades. It was confirmed that homozygosity for the DMRT3 nonsense mutation relates to the ability to pace. It further had a favourable effect on scores in breeding field tests for the lateral gait tölt, demonstrated by better beat quality, speed capacity and suppleness. Horses with the CA genotype had on the other hand significantly higher scores for walk, trot, canter and gallop, and they performed better beat and suspension in trot and gallop. These results indicate that the AA genotype reinforces the coordination of ipsilateral legs, with the subsequent negative effect on the synchronized movement of diagonal legs compared with the CA genotype. The frequency of the A-allele has increased in recent decades with a corresponding decrease in the frequency of the C-allele. The estimated frequency of the A-allele in the Icelandic horse population in 2012 was 0.94. Selective breeding for lateral gaits in the Icelandic horse population has apparently altered the frequency of DMRT3 genotypes with a predicted loss of the C-allele in relatively few years. The results have practical implications for breeding and training of Icelandic horses and other gaited horse breeds.