Mining Butterflies in Streaming Graphs

This thesis introduces two main-memory systems sGrapp and sGradd for performing the fundamental analytic tasks of biclique counting and concept drift detection over a streaming graph. A data-driven heuristic is used to architect the systems. To this end, initially, the growth patterns of bipartite streaming graphs are mined and the emergence principles of streaming motifs are discovered. Next, the discovered principles are (a) explained by a graph generator called sGrow; and (b) utilized to establish the requirements for efficient, effective, explainable, and interpretable management and processing of streams. sGrow is used to benchmark stream analytics, particularly in the case of concept drift detection. sGrow displays robust realization of streaming growth patterns independent of initial conditions, scale and temporal characteristics, and model configurations. Extensive evaluations confirm the simultaneous effectiveness and efficiency of sGrapp and sGradd. sGrapp achieves mean absolute percentage error up to 0.05/0.14 for the cumulative butterfly count in streaming graphs with uniform/non-uniform temporal distribution and a processing throughput of 1.5 million data records per second. The throughput and estimation error of sGrapp are 160x higher and 0.02x lower than baselines. sGradd demonstrates an improving performance over time, achieves zero false detection rates when there is not any drift and when drift is already detected, and detects sequential drifts in zero to a few seconds after their occurrence regardless of drift intervals

Emergence of global synchronization in directed excitatory networks of type I neurons

The collective behaviour of neural networks depends on the cellular and synaptic properties of the neurons. The phase-response curve (PRC) is an experimentally obtainable measure of cellular properties that quantifies the shift in the next spike time of a neuron as a function of the phase at which stimulus is delivered to that neuron. The neuronal PRCs can be classified as having either purely positive values (type I) or distinct positive and negative regions (type II). Networks of type 1 PRCs tend not to synchronize via mutual excitatory synaptic connections. We study the synchronization properties of identical type I and type II neurons, assuming unidirectional synapses. Performing the linear stability analysis and the numerical simulation of the extended Kuramoto model, we show that feedforward loop motifs favour synchronization of type I excitatory and inhibitory neurons, while feedback loop motifs destroy their synchronization tendency. Moreover, large directed networks, either without feedback motifs or with many of them, have been constructed from the same undirected backbones, and a high synchronization level is observed for directed acyclic graphs with type I neurons. It has been shown that, the synchronizability of type I neurons depends on both the directionality of the network connectivity and the topology of its undirected backbone. The abundance of feedforward motifs enhances the synchronizability of the directed acyclic graphs

The Kuramoto model in complex networks

181 pages, 48 figures. In Press, Accepted Manuscript, Physics Reports 2015 Acknowledgments We are indebted with B. Sonnenschein, E. R. dos Santos, P. Schultz, C. Grabow, M. Ha and C. Choi for insightful and helpful discussions. T.P. acknowledges FAPESP (No. 2012/22160-7 and No. 2015/02486-3) and IRTG 1740. P.J. thanks founding from the China Scholarship Council (CSC). F.A.R. acknowledges CNPq (Grant No. 305940/2010-4) and FAPESP (Grants No. 2011/50761-2 and No. 2013/26416-9) for financial support. J.K. would like to acknowledge IRTG 1740 (DFG and FAPESP).Peer reviewedPreprin

Are feedback loops destructive to synchronization?

We study the effects of directionality on synchronization of dynamical networks. Performing the linear stability analysis and the numerical simulation of the Kuramoto model in directed networks, we show that balancing in- and out-degrees of all nodes enhances the synchronization of sparse networks, especially in networks with high clustering coefficient and homogeneous degree distribution. Furthermore, by omitting all the feedback loops, we show that while hierarchical directed acyclic graphs are structurally highly synchronizable, their global synchronization is too sensitive to the choice of natural frequencies and is strongly affected by noise

Dont eat me/eat me signals as a novel strategy in cancer immunotherapy.

Cancer stands as one of the prominent global causes of death, with its incidence burden continuously increasing, leading to a substantial rise in mortality rates. Cancer treatment has seen the development of various strategies, each carrying its drawbacks that can negatively impact the quality of life for cancer patients. The challenge remains significant within the medical field to establish a definitive cancer treatment that minimizes complications and limitations. In the forthcoming years, exploring new strategies to surmount the failures in cancer treatment appears to be an unavoidable pursuit. Among these strategies, immunology-based ones hold substantial promise in combatting cancer and immune-related disorders. A particular subset of this approach identifies eat me and Dont eat me signals in cancer cells, contrasting them with their counterparts in non-cancerous cells. This distinction could potentially mark a significant breakthrough in treating diverse cancers. By delving into signal transduction and engineering novel technologies that utilize distinct eat me and Dont eat me signals, a valuable avenue may emerge for advancing cancer treatment methodologies. Macrophages, functioning as vital components of the immune system, regulate metabolic equilibrium, manage inflammatory disorders, oversee fibrosis, and aid in the repair of injuries. However, in the context of tumor cells, the overexpression of Dont eat me signals like CD47, PD-L1, and beta-2 microglobulin (B2M), an anti-phagocytic subunit of the primary histocompatibility complex class I, enables these cells to evade macrophages and proliferate uncontrollably. Conversely, the presentation of an eat me signal, such as Phosphatidylserine (PS), along with alterations in charge and glycosylation patterns on the cellular surface, modifications in intercellular adhesion molecule-1 (ICAM-1) epitopes, and the exposure of Calreticulin and PS on the outer layer of the plasma membrane represent universally observed changes on the surface of apoptotic cells, preventing phagocytosis from causing harm to adjacent non-tumoral cells. The current review provides insight into how signaling pathways and immune cells either stimulate or obstruct these signals, aiming to address challenges that may arise in future immunotherapy research. A potential solution lies in combination therapies targeting the eat me and Dont eat me signals in conjunction with other targeted therapeutic approaches. This innovative strategy holds promise as a novel avenue for the future treatment of cancer