“Quinone Millipedes” Reconsidered: Evidence for a Mosaic-Like Taxonomic Distribution of Phenol-Based Secretions across the Julidae

The defensive chemistry of juliformian millipedes is characterized mainly by benzoquinones (”quinone millipedes”), whereas the secretions of the putative close outgroup Callipodida are considered to be exclusively phenolic. We conducted a chemical screening of julid secretions for phenolic content. Most species from tribes Cylindroiulini (15 species examined), Brachyiulini (5 species examined), Leptoiulini (15 species examined), Uncigerini (2 species examined), Pachyiulini (3 species examined), and Ommatoiulini (2 species examined) had non-phenolic, in most cases exclusively benzoquinonic secretions. In contrast, tribes Cylindroiulini, Brachyiulini, and Leptoiulini also contained representatives with predominantly phenol-based exudates. In detail, p-cresol was a major compound in the secretions of the cylindroiulines Styrioiulus pelidnus and S. styricus (p-cresol content 93 %) and an undetermined Cylindroiulus species (p-cresol content 51 %), in the brachyiulines Brachyiulus lusitanus (p-cresol content 21 %) and Megaphyllum fagorum (p-cresol content 92 %), as well as in an undescribed Typhloiulus species (p-cresol content 32 %, Leptoiulini). In all species, p-cresol was accompanied by small amounts of phenol. The secretion of M. fagorum was exclusively phenolic, whereas phenols were accompanied by benzoquinones in all other species. This is the first incidence of clearly phenol-dominated secretions in the Julidae. We hypothesize a shared biosynthetic route to phenols and benzoquinones, with benzoquinones being produced from phenolic precursors. The patchy taxonomic distribution of phenols documented herein supports multiple independent regression events in a common pathway of benzoquinone synthesis rather than multiple independent incidences of phenol biosynthesis.     

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.     

INFERENCE PROCEDURES FOR FUZZY KNOWLEDGE REPRESENTATION SCHEME

This article presents a formal model of the knowledge representation scheme based on the fuzzy Petri net (FPN) theory. The model is represented as a 13-tuple consisting of the components of the FPN, two functions that give semantic meanings to the scheme and a set of contradictions. For the scheme, called the knowledge representation scheme based on the fuzzy Petri nets theory (KRFPN) the fuzzy inheritance and fuzzy recognition-inference procedures based on the dynamical properties of the FPN, are described in detail. The upper-time complexity of both the proposed inference algorithms is O(nm), where n is the number of places (concepts) and m is the number of transitions (relations) in the scheme. Illustrative examples of the fuzzy inheritance and the fuzzy recognition algorithms for the knowledge base, designed by the KRFPN, are given.     

Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion

This paper proposes an approach for decentralized fault detection and diagnosis in process monitoring sensor networks. The sensor network is decomposed into multiple, potentially overlapping, blocks using the Sparse Principal Component Analysis algorithm. Local predictions are generated at each block using Support Vector Machine classifiers. The local predictions are then fused via a Maximum Entropy algorithm. Empirical studies on the benchmark Tennessee Eastman Process data demonstrated that the proposed decentralized approach achieves accuracy comparable to that of the fully centralized approach, while offering benefits in terms of fault tolerance, reusability, and scalability.     

Supervised clustering of label ranking data using label preference information

This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers’ feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.     

Ethanol interactions with other cytochrome P450 substrates including drugs, xenobiotics, and carcinogens

Chronic ethanol abuse is associated with increased activity of the microsomal ethanol-oxidizing system. This effect is due primarily to induction by ethanol of a specific cytochrome P450 (CYP2E1) responsible for enhanced oxidation of ethanol and other P450 substrates and, consequently, for metabolic tolerance to these substances. Furthermore, cytochrome 450 induction increases the activation of numerous xenobiotics to toxic metabolites and of chemical carcinogens to reactive metabolites, thereby accelerating their adverse effects. Microsomal enzyme induction has been associated with increased reactive oxygen species production and enhanced lipid peroxidation, as well as with decreased enzymatic and nonenzymatic scavenger activity, providing another possible explanation for ethanol-mediated toxicity. Yet another effect of chronic alcohol abuse is chronic immune system activation, which is the mechanism underlying alcohol-related liver disease. The metabolism of steroids and vitamins is catalyzed by P450 and is altered in chronic alcoholics. This article reviews recent advances in the understanding of ethanol interactions with drugs, toxic agents, and carcinogens, as well as with steroids and vitamins.     

The Use of Object-Oriented Modeling for Water Resources Planning in Egypt

Water problems are omnipresent and are already becoming a limiting factor in the development of many countries. Currently the balance between the available and required water in Egypt is fragile. Any movement away from the balancing point means either less ambitious economic development or depletion of the resources and degradation of the environment. The continuing revolution in computer hardware and software is expected to provide more insight into the water problems and to alleviate some of the future water crises. In this paper we have investigated potential benefits which can be accrued from the application of object-oriented modeling in water resources.