Exceptional Preferences Mining

Exceptional Preferences Mining (EPM) is a crossover between two subfields of datamining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where the preference relations between subsets of the labels significantly deviate from the norm; a variant of Subgroup Discovery, with rankings as the (complex) target concept. We employ three quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes 'exceptional' varies with the quality measure: the first gauges exceptional overall ranking behavior, the second indicates whether a particular label stands out from the rest, and the third highlights subgroups featuring unusual pairwise label ranking behavior. As proof of concept, we explore five datasets. The results confirm that the new task EPM can deliver interesting knowledge. The results also illustrate how the visualization of the preferences in a Preference Matrix can aid in interpreting exceptional preference subgroups

Antioxidant and hepatoprotective role of selenium against silver nanoparticles [Corrigendum]

Ansar S, Alshehri SM, Abudawood M, Hamed SS, Ahamad T. Antioxidant and hepatoprotective role of sele-nium against silver nanoparticles. Int J Nanomedicine. 2017;12:7789–7797. On page 7796, the Acknowledgment is incorrect, it should have been: The authors extend their appreciation to the Deanship of Scientific Research, King Saud University, for funding this work through the Undergraduate Student Research Support Program. Read the original article.&nbsp

An Insight into the Impact of Serum Tellurium, Thallium, Osmium and Antimony on the Antioxidant/Redox Status of PCOS Patients: A Comprehensive Study

Humans exploit heavy metals for various industrial and economic reasons. Although some heavy metals are essential for normal physiology, others such as Tellurium (Te), Thallium (TI), antimony (Sb), and Osmium (Os) are highly toxic and can lead to Polycystic Ovarian Syndrome (PCOS), a common female factor of infertility. The current study was undertaken to determine levels of the heavy metals TI, Te, Sb and Os in serum of PCOS females (n = 50) compared to healthy non-PCOS controls (n = 56), and to relate such levels with Total Antioxidant Capacity (TAC), activity of key antioxidant enzymes, oxidative stress marker levels and redox status. PCOS serum samples demonstrated significantly higher levels of TI, Te, Sb and Os and diminished TAC compared to control (p p < 0.001 for all parameters). Additionally, a significant negative correlation was found between the elevated levels of heavy metals and TAC, indicative of the role of metal-induced oxidative stress as a prominent phenomenon associated with the pathophysiology of the underlying PCOS. Data obtained in the study suggest toxic metals as risk factors causing PCOS, and thus protective measures should be considered to minimize exposure to prevent such reproductive anomalies

An Insight into the Impact of Serum Tellurium, Thallium, Osmium and Antimony on the Antioxidant/Redox Status of PCOS Patients: A Comprehensive Study

Humans exploit heavy metals for various industrial and economic reasons. Although some heavy metals are essential for normal physiology, others such as Tellurium (Te), Thallium (TI), antimony (Sb), and Osmium (Os) are highly toxic and can lead to Polycystic Ovarian Syndrome (PCOS), a common female factor of infertility. The current study was undertaken to determine levels of the heavy metals TI, Te, Sb and Os in serum of PCOS females (n = 50) compared to healthy non-PCOS controls (n = 56), and to relate such levels with Total Antioxidant Capacity (TAC), activity of key antioxidant enzymes, oxidative stress marker levels and redox status. PCOS serum samples demonstrated significantly higher levels of TI, Te, Sb and Os and diminished TAC compared to control (p &lt; 0.001). Furthermore, there was significant inhibition of SOD, CAT and several glutathione-related enzyme activities in sera of PCOS patients with concurrent elevations in superoxide anions, hydrogen and lipid peroxides, and protein carbonyls, along with disrupted glutathione homeostasis compared to those of controls (p &lt; 0.001 for all parameters). Additionally, a significant negative correlation was found between the elevated levels of heavy metals and TAC, indicative of the role of metal-induced oxidative stress as a prominent phenomenon associated with the pathophysiology of the underlying PCOS. Data obtained in the study suggest toxic metals as risk factors causing PCOS, and thus protective measures should be considered to minimize exposure to prevent such reproductive anomalies

Anytime Subgroup Discovery in Numerical Domains with Guarantees

International audienceSubgroup discovery is the task of discovering patterns that accurately discriminate a class label from the others. Existing approaches can uncover such patterns either through an exhaustive or an approximate exploration of the pattern search space. However, an exhaustive exploration is generally unfeasible whereas approximate approaches do not provide guarantees bounding the error of the best pattern quality nor the exploration progression ("How far are we of an exhaustive search"). We design here an algorithm for mining numerical data with three key properties w.r.t. the state of the art: (i) It yields progressively interval patterns whose quality improves over time; (ii) It can be interrupted anytime and always gives a guarantee bounding the error on the top pattern quality and (iii) It always bounds a distance to the exhaustive exploration. After reporting experimentations showing the effectiveness of our method, we discuss its generalization to other kinds of patterns

Anytime Subgroup Discovery in Numerical Domains with Guarantees - Technical Report

Subgroup discovery is the task of discovering patterns that accurately discriminate a class label from the others. Existing approaches can uncover such patterns either through an exhaustive or an approximate exploration of the pattern search space. However, an exhaustive exploration is generally unfeasible whereas approximate approaches do not provide guarantees bounding the error of the best pattern quality nor the exploration progression ("How far are we of an exhaustive search"). We design here an algorithm for mining numerical data with three key properties w.r.t. the state of the art: (i) It yields progressively interval patterns whose quality improves over time; (ii) It can be interrupted anytime and always gives a guarantee bounding the error on the top pattern quality and (iii) It always bounds a distance to the exhaustive exploration. After reporting experimentations showing the effectiveness of our method, we discuss its generalization to other kinds of patterns