Good guy or bad guy? The duality of wild-type p53 in hormone-dependent breast cancer origin, treatment, and recurrence

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. “Lactation is at one point perilously near becoming a cancerous process if it is at all arrested”, Beatson, 1896. Most breast cancers arise from the milk-producing cells that are characterized by aberrant cellular, molecular, and epigenetic translation. By understanding the underlying molecular disruptions leading to the origin of cancer, we might be able to design novel strategies for more efficacious treatments or, ambitiously, divert the cancerous process. It is an established reality that full-term pregnancy in a young woman provides a lifetime reduction in breast cancer risk, whereas delay in full-term pregnancy increases short-term breast cancer risk and the probability of latent breast cancer development. Hormonal activation of the p53 protein (encode by the TP53 gene) in the mammary gland at a critical time in pregnancy has been identified as one of the most important determinants of whether the mammary gland develops latent breast cancer. This review discusses what is known about the protective influence of female hormones in young parous women, with a specific focus on the opportune role of wild-type p53 reprogramming in mammary cell differentiation. The importance of p53 as a protector or perpetrator in hormone-dependent breast cancer, resistance to treatment, and recurrence is also explored

L-Drawings of Directed Graphs

We introduce L-drawings, a novel paradigm for representing directed graphs aiming at combining the readability features of orthogonal drawings with the expressive power of matrix representations. In an L-drawing, vertices have exclusive $x$- and $y$-coordinates and edges consist of two segments, one exiting the source vertically and one entering the destination horizontally. We study the problem of computing L-drawings using minimum ink. We prove its NP-completeness and provide a heuristics based on a polynomial-time algorithm that adds a vertex to a drawing using the minimum additional ink. We performed an experimental analysis of the heuristics which confirms its effectiveness.Comment: 11 pages, 7 figure

Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions

Physics-informed Neural Networks (PINNs) have been shown as a promising approach for solving both forward and inverse problems of partial differential equations (PDEs). Meanwhile, the neural operator approach, including methods such as Deep Operator Network (DeepONet) and Fourier neural operator (FNO), has been introduced and extensively employed in approximating solution of PDEs. Nevertheless, to solve problems consisting of sharp solutions poses a significant challenge when employing these two approaches. To address this issue, we propose in this work a novel framework termed Operator Learning Enhanced Physics-informed Neural Networks (OL-PINN). Initially, we utilize DeepONet to learn the solution operator for a set of smooth problems relevant to the PDEs characterized by sharp solutions. Subsequently, we integrate the pre-trained DeepONet with PINN to resolve the target sharp solution problem. We showcase the efficacy of OL-PINN by successfully addressing various problems, such as the nonlinear diffusion-reaction equation, the Burgers equation and the incompressible Navier-Stokes equation at high Reynolds number. Compared with the vanilla PINN, the proposed method requires only a small number of residual points to achieve a strong generalization capability. Moreover, it substantially enhances accuracy, while also ensuring a robust training process. Furthermore, OL-PINN inherits the advantage of PINN for solving inverse problems. To this end, we apply the OL-PINN approach for solving problems with only partial boundary conditions, which usually cannot be solved by the classical numerical methods, showing its capacity in solving ill-posed problems and consequently more complex inverse problems.Comment: Preprint submitted to Elsevie

Tracking advanced persistent threats in critical infrastructures through opinion dynamics

Advanced persistent threats pose a serious issue for modern industrial environments, due to their targeted and complex attack vectors that are difficult to detect. This is especially severe in critical infrastructures that are accelerating the integration of IT technologies. It is then essential to further develop effective monitoring and response systems that ensure the continuity of business to face the arising set of cyber-security threats. In this paper, we study the practical applicability of a novel technique based on opinion dynamics, that permits to trace the attack throughout all its stages along the network by correlating different anomalies measured over time, thereby taking the persistence of threats and the criticality of resources into consideration. The resulting information is of essential importance to monitor the overall health of the control system and cor- respondingly deploy accurate response procedures. Advanced Persistent Threat Detection Traceability Opinion Dynamics.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Mammalian sphingosine kinase (SphK) isoenzymes and isoform expression: Challenges for SphK as an oncotarget

Copyright: © Hatoum et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The various sphingosine kinase (SphK) isoenzymes (isozymes) and isoforms, key players in normal cellular physiology, are strongly implicated in cancer and other diseases. Mutations in SphKs, that may justify abnormal physiological function, have not been recorded. Nonetheless, there is a large and growing body of evidence demonstrating the contribution of gain or loss of function and the imbalance in the SphK/S1P rheostat to a plethora of pathological conditions including cancer, diabetes and inflammatory diseases. SphK is expressed as two isozymes SphK1 and SphK2, transcribed from genes located on different chromosomes and both isozymes catalyze the phosphorylation of sphingosine to S1P. Expression of each SphK isozyme produces alternately spliced isoforms. In recent years the importance of the contribution of SpK1 expression to treatment resistance in cancer has been highlighted and, additionally, differences in treatment outcome appear to also be dependent upon SphK isoform expression. This review focuses on an exciting emerging area of research involving SphKs functions, expression and subcellular localization, highlighting the complexity of targeting SphK in cancer and also comorbid diseases. This review also covers the SphK isoenzymes and isoforms from a historical perspective, from their first discovery in murine species and then in humans, their role(s) in normal cellular function and in disease processes, to advancement of SphK as an oncotarget

Mammalian sphingosine kinase (SphK) isoenzymes and isoform expression: challenges for SphK as an oncotarget.

The various sphingosine kinase (SphK) isoenzymes (isozymes) and isoforms, key players in normal cellular physiology, are strongly implicated in cancer and other diseases. Mutations in SphKs, that may justify abnormal physiological function, have not been recorded. Nonetheless, there is a large and growing body of evidence demonstrating the contribution of gain or loss of function and the imbalance in the SphK/S1P rheostat to a plethora of pathological conditions including cancer, diabetes and inflammatory diseases. SphK is expressed as two isozymes SphK1 and SphK2, transcribed from genes located on different chromosomes and both isozymes catalyze the phosphorylation of sphingosine to S1P. Expression of each SphK isozyme produces alternately spliced isoforms. In recent years the importance of the contribution of SpK1 expression to treatment resistance in cancer has been highlighted and, additionally, differences in treatment outcome appear to also be dependent upon SphK isoform expression. This review focuses on an exciting emerging area of research involving SphKs functions, expression and subcellular localization, highlighting the complexity of targeting SphK in cancer and also comorbid diseases. This review also covers the SphK isoenzymes and isoforms from a historical perspective, from their first discovery in murine species and then in humans, their role(s) in normal cellular function and in disease processes, to advancement of SphK as an oncotarget

“Dicing and splicing” sphingosine kinase and relevance to cancer

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Sphingosine kinase (SphK) is a lipid enzyme that maintains cellular lipid homeostasis. Two SphK isozymes, SphK1 and SphK2, are expressed from different chromosomes and several variant isoforms are expressed from each of the isozymes, allowing for the multi-faceted biological diversity of SphK activity. Historically, SphK1 is mainly associated with oncogenicity, however in reality, both SphK1 and SphK2 isozymes possess oncogenic properties and are recognized therapeutic targets. The absence of mutations of SphK in various cancer types has led to the theory that cancer cells develop a dependency on SphK signaling (hyper-SphK signaling) or “non-oncogenic addiction”. Here we discuss additional theories of SphK cellular mislocation and aberrant “dicing and splicing” as contributors to cancer cell biology and as key determinants of the success or failure of SphK/S1P (sphingosine 1 phosphate) based therapeutics

Final Technical Report - Stochastic Nonlinear Data-Reduction Methods with Detection and Prediction of Critical Rare Event

In this project, the collective efforts of all co-PIs aim to address three current limitations in modeling stochastic systems: (1) the inputs are mostly based on ad hoc models, (2) the number of independent parameters is very high, and (3) rare and critical events are difficult to capture with existing algorithm

Spinoza

"Spinoza", second edition. Encyclopedia entry for the Springer Encyclopedia of EM Phil and the Sciences, ed. D. Jalobeanu and C. T. Wolfe