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Lipid profiles in biological fluids from patients with Parkinson’s disease (PD) are increasingly investigated in search of biomarkers. However, the lipid profiles in genetic PD remain to be determined, a gap of knowledge of particular interest in PD associated with mutant α-synuclein (SNCA), given the known relationship between this protein and lipids. The objective of this research is to identify serum lipid composition from SNCA A53T mutation carriers and to compare these alterations to those found in cells and transgenic mice carrying the same genetic mutation. We conducted an unbiased lipidomic analysis of 530 lipid species from 34 lipid classes in serum of 30 participants with SNCA mutation with and without PD and 30 healthy controls. The primary analysis was done between 22 PD patients with SNCA+ (SNCA+/PD+) and 30 controls using machine-learning algorithms and traditional statistics. We also analyzed the lipid composition of human clonal-cell lines and tissue from transgenic mice overexpressing the same SNCA mutation. We identified specific lipid classes that best discriminate between SNCA+/PD+ patients and healthy controls and found certain lipid species, mainly from the glycerophosphatidylcholine and triradylglycerol classes, that are most contributory to this discrimination. Most of these alterations were also present in human derived cells and transgenic mice carrying the same mutation. Our combination of lipidomic and machine learning analyses revealed alterations in glycerophosphatidylcholine and triradylglycerol in sera from PD patients as well as cells and tissues expressing mutant α-Syn. Further investigations are needed to establish the pathogenic significance of these α-Syn-associated lipid changes.

Although recent studies have shown that a Bayesian network classifier (BNC) that maximizes the classification accuracy (i.e., minimizes the 0/1 loss function) is a powerful tool in both knowledge representation and classification, this classifier: (1) focuses on the majority class and, therefore, misclassifies minority classes; (2) is usually uninformative about the distribution of misclassifications; and (3) is insensitive to error severity (making no distinction between misclassification types). In this study, we propose to learn the structure of a BNC using an information measure (IM) that jointly maximizes the classification accuracy and information, motivate this measure theoretically, and evaluate it compared with six common measures using various datasets. Using synthesized confusion matrices, twenty-three artificial datasets, seventeen UCI datasets, and different performance measures, we show that an IM-based BNC is superior to BNCs learned using the other measures—especially for ordinal classification (for which accounting for the error severity is important) and/or imbalanced problems (which are most real-life classification problems)—and that it does not fall behind state-of-the-art classifiers with respect to accuracy and amount of information provided. To further demonstrate its ability, we tested the IM-based BNC in predicting the severity of motorcycle accidents of young drivers and the disease state of ALS patients—two class-imbalance ordinal classification problems—and show that the IM-based BNC is accurate also for the minority classes (fatal accidents and severe patients) and not only for the majority class (mild accidents and mild patients) as are other classifiers, providing more informative and practical classification results. Based on the many experiments we report on here, we expect these advantages to exist for other problems in which both accuracy and information should be maximized, the data is imbalanced, and/or the problem is ordinal, whether the classifier is a BNC or not. Our code, datasets, and results are publicly available http://www.ee.bgu.ac.il/~boaz/software .

Understanding land use in urban areas, from the perspective of social function, is beneficial for a variety of fields, including urban and highway planning, advertising, and business. However, big cities with complex social dynamics and rapid development complicate the task of understanding these social functions. In this paper, we analyze and interpret human social function in urban areas as reflected in cellular communication usage patterns. We base our analysis on digital traces left by mobile phone users, and from this raw data, we derive a varied collection of features that illuminate the social behavior of each land use. We divide space and time into basic spatiotemporal units and classify them according to their land use. We categorize land uses with a leveled hierarchy of semantic categories that include different levels of detail resolution. We apply the above methodology to a dataset consisting of 62 days of cellular data recorded in nine cities in the Tel Aviv district. The methodology proved beneficial with an accuracy rate ranging from 84 to 91%, dependent on land use label resolution. In addition, analyzing the results sheds light on some of the limitations of relying solely on cellular communication as a data resource. We discuss some of these problems and offer applicable solutions.

Human mobility patterns reflect many aspects of life, from the global spread of infectious diseases to urban planning and daily commute patterns. In recent years, the prevalence of positioning methods and technologies, such as the global positioning system, cellular radio tower geo-positioning, and WiFi positioning systems, has driven efforts to collect human mobility data and to mine patterns of interest within these data in order to promote the development of location-based services and applications. The efforts to mine significant patterns within large-scale, high-dimensional mobility data have solicited use of advanced analysis techniques, usually based on machine learning methods, and therefore, in this paper, we survey and assess different approaches and models that analyze and learn human mobility patterns using mainly machine learning methods. We categorize these approaches and models in a taxonomy based on their positioning characteristics, the scale of analysis, the properties of the modeling approach, and the class of applications they can serve. We find that these applications can be categorized into three classes: user modeling, place modeling, and trajectory modeling, each class with its characteristics. Finally, we analyze the short-term trends and future challenges of human mobility analysis.

in situ hybridisation (FISH) signals. Highly-accurate classification of valid signals and artifacts of several cytogenetic probes (colours) is required for detecting abnormalities in FISH images. More than 3100 FISH signals are classified by each of the techniques into colour and as real or artifact with accuracies of around 98% and 88%, respectively. The results of the comparison also show a trade-off between simplicity represented by the naive Bayesian classifier, and high classification performance represented by the other techniques.

The initialisation of a neural network implementation of Sammon's mapping, either randomly or based on the principal components (PCs) of the sample covariance matrix, is experimentally investigated. When PCs are employed, fewer experiments are needed and the network configuration can be set precisely without trial-and-error experimentation. Tested on five real-world databases, it is shown that very few PCs are required to achieve a shorter training period, lower mapping error and higher classification accuracy, compared with those based on random initialisation.