Brain ischemia leading to stroke is a major cause of disability in developed countries. Therapeutic strategies have most commonly focused on protecting neurons from ischemic damage. However, ischemic damage to white matter causes oligodendrocyte death, myelin disruption, and axon dysfunction, and it is partially mediated by glutamate excitotoxicity. We have previously demonstrated that oligodendrocytes express ionotropic purinergic receptors. The objective of this study was to investigate the role of purinergic signaling in white matter ischemia. We show that, in addition to glutamate, enhanced ATP signaling during ischemia is also deleterious to oligodendrocytes and myelin, and impairs white matter function. Thus, ischemic oligodendrocytes in culture display an inward current and cytosolic Ca(2+) overload, which is partially mediated by P2X7 receptors. Indeed, oligodendrocytes release ATP after oxygen and glucose deprivation through the opening of pannexin hemichannels. Consistently, ischemia-induced mitochondrial depolarization as well as oxidative stress culminating in cell death are partially reversed by P2X7 receptor antagonists, by the ATP degrading enzyme apyrase and by blockers of pannexin hemichannels. In turn, ischemic damage in isolated optic nerves, which share the properties of brain white matter, is greatly attenuated by all these drugs. Ultrastructural analysis and electrophysiological recordings demonstrated that P2X7 antagonists prevent ischemic damage to oligodendrocytes and myelin, and improved action potential recovery after ischemia. These data indicate that ATP released during ischemia and the subsequent activation of P2X7 receptor is critical to white matter demise during stroke and point to this receptor type as a therapeutic target to limit tissue damage in cerebrovascular diseases.

Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking), harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Then, we show that our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts.

Computing the frequency of small subgraphs on a large network is a computationally hard task. This is, however, an important graph mining primitive, with several applications, and here we present a novel multicore parallel algorithm for this task. At the core of our methodology lies a state-of-the-art data structure, the g-trie, which represents a collection of subgraphs and allows for a very efficient sequential search. Our implementation was done using Pthreads and can run on any multicore personal computer. We employ a diagonal work sharing strategy to dynamically and effectively divide work among threads during the execution. We assess the performance of our Pthreads implementation on a set of representative networks from various domains and with diverse topological features. For most networks, we obtain a speedup of over 50 for 64 cores and an almost linear speedup up to 32 cores, showcasing the flexibility and scalability of our algorithm. This paves the way for the usage of such counting algorithms on larger subgraph and network sizes without the obligatory access to a cluster.