Neuronal nitric oxide synthase (nNOS) inhibitors are effective in preclinical models of many neurological disorders. In this study, two related series of compounds, 3,4-dihydroquinolin-2(1H)-one and 1,2,3,4-tetrahydroquinoline, containing a 6-substituted thiophene amidine group were synthesized and evaluated as inhibitors of human nitric oxide synthase (NOS). A structure–activity relationship (SAR) study led to the identification of a number of potent and selective nNOS inhibitors. Furthermore, a few representative compounds were shown to possess druglike properties, features that are often difficult to achieve when designing nNOS inhibitors. Compound (S)-35, with excellent potency and selectivity for nNOS, was shown to fully reverse thermal hyperalgesia when given to rats at a dose of 30 mg/kg intraperitonieally (ip) in the L5/L6 spinal nerve ligation model of neuropathic pain (Chung model). In addition, this compound reduced tactile hyperesthesia (allodynia) after oral administration (30 mg/kg) in a rat model of dural inflammation relevant to migraine pain.

Financial crimes affect millions of people every year and financial institutions must employ methods to protect themselves and their customers. The use of statistical methods to address these problems faces many challenges. Financial crimes are rare events that lead to extreme class imbalances. Criminals deliberately attempt to conceal the nature of their actions and quickly change their strategies over time, resulting in class overlap and concept drift. In some cases, legal constraints and investigation delays make it impossible to actually verify suspected crimes in a timely manner, resulting in class mislabeling or unknown labels. In addition, the volume and complexity of financial data require algorithms to be not only effective, but also efficiently trained and executed. This article focuses on two important types of financial crimes: fraud and money laundering. It discusses some of the traditional statistical techniques that have been applied as well as more recent machine learning and data mining algorithms. The goal of the article is to introduce the subject and to provide a survey of broad classes of methodologies accompanied by selected illustrative examples.

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Ardent défenseur de la mission pédagogique du musée d'histoire naturelle, l'auteur a eu maintes occasions de mettre ses idées en pratique en qualité de fondateur, puis de directeur, depuis plus de vingt ans, du Musée national d'histoire naturelle de New Delhi. Vice‐président du Comité international pour les musées et collections d'histoire naturelle de l'ICOM et de l'Association des musées de l'Inde, S. M. Nair est conseiller du Fonds mondial pour la nature pour l'Inde. Il doit sa consécration internationale à l'attribution de la J. D. Rockefeller III Fund Fellowship, de la Homi Bhabha Fellowship et de la Smithsonian National Museum Act Fellowship. Le gouvernement de l'Inde lui a décerné le Distinguished Scientist Award 1993–1994 pour sa contribution à l'éducation relative à l'environnement et à la muséologie.

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