c-FLIP, a master anti-apoptotic regulator

Cellular FLICE (FADD-like IL-1β-converting enzyme)-inhibitory protein (c-FLIP) is a master anti-apoptotic regulator and resistance factor that suppresses tumor necrosis factor-α (TNF-α), Fas-L, and TNF-related apoptosis-inducing ligand (TRAIL)-induced apoptosis, as well as apoptosis triggered by chemotherapy agents in malignant cells. c-FLIP is expressed as long (c-FLIP(L)), short (c-FLIP(S)), and c-FLIP(R) splice variants in human cells. c-FLIP binds to FADD and/or caspase-8 or -10 and TRAIL receptor 5 (DR5) in a ligand-dependent and -independent fashion and forms an apoptosis inhibitory complex (AIC). This interaction in turn prevents death-inducing signaling complex (DISC) formation and subsequent activation of the caspase cascade. c-FLIP(L) and c-FLIP(S) are also known to have multifunctional roles in various signaling pathways, as well as activating and/or upregulating several cytoprotective and pro-survival signaling proteins including Akt, ERK, and NF-kB. Upregulation of c-FLIP has been found in various tumor types, and its silencing has been shown to restore apoptosis triggered by cytokines and various chemotherapeutic agents. Hence, c-FLIP is an important target for cancer therapy. For example, small interfering RNAs (siRNAs) that specifically knockdown the expression of c-FLIP(L) in diverse human cancer cell lines augmented TRAIL-induced DISC recruitment and increased the efficacy of chemotherapeutic agents, thereby enhancing effector caspase stimulation and apoptosis. Moreover, small molecules causing degradation of c-FLIP as well as decreasing mRNA and protein levels of c-FLIP(L) and c-FLIP(S) splice variants have been found, and much effort is focused on developing other c-FLIP-targeted cancer therapies. This review focuses on (1) the anti-apoptotic role of c-FLIP splice variants in preventing apoptosis and inducing cytokine and chemotherapy drug resistance, (2) the molecular mechanisms and factors that regulate c-FLIP expression, and (3) modulation of c-FLIP expression and function to eliminate cancer cells or increase the efficacy of anticancer agents. This article is part of a Special Issue entitled "Apoptosis: Four Decades Later"

Indole-3-carbinol suppresses NF-κB activity and stimulates the p53 pathway in pre-B acute lymphoblastic leukemia cells

B cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most common type of cancer in children. Dramatic improvements in primary therapy for childhood ALL have led to an overall cure rate of 80 , providing opportunities for innovative combined-modality strategies that would increase cure rates while reducing the toxic side effects of current intensive regimens. In this study, we report that indole-3-carbinol (I3C), a natural phytochemical found in cruciferous vegetables, had anti-leukemic properties in BCP-ALL NALM-6 cells. I3C induced cell growth inhibition by G1 cell cycle arrest and triggered apoptosis in a dose- and time-dependent manner. p53, p21, and Bax proteins showed increased expression after I3C treatment. Real-time PCR analysis of pro-apoptotic p53 target genes revealed up-regulation of PUMA, NOXA, and Apaf-1. I3C also suppressed constitutive nuclear factor-κB (NF-κB) activation and inhibited the protein expression of NF-kappa B-regulated antiapoptotic (IAP1, Bcl-xL, Bcl-2, XIAP) and proliferative (c-Myc) gene products. Coadministration of I3C with the topoisomerase II inhibitor, doxorubicin, potentiates cytotoxic effects compared with either agent alone. Apoptosis induction by the drug combination was associated with enhanced caspase-9 activation and PARP cleavage. Furthermore, I3C abolished doxorubicin-induced NF-κB activity as evidenced by decreased nuclear accumulation of p65, inhibition of IκBα phosphorylation and its degradation, and decreased NF-κB DNA-binding activity. Western blot analysis revealed that doxorubicin-induced Bcl-2 protein expression was inhibited by I3C. Overall, our results indicated that using nontoxic agents, such as I3C, in combination with anthracyclines might provide a new insight into the development of novel combination therapies in childhood BCP-ALL. © 2015, International Society of Oncology and BioMarkers (ISOBM)

Emerging targets for glioblastoma stem cell therapy

Glioblastoma multiforme (GBM), designated as World Health Organization (WHO) grade IV astrocytoma, is a lethal and therapy-resistant brain cancer comprised of several tumor cell subpopulations, including GBM stem cells (GSCs) which are believed to contribute to tumor recurrence following initial response to therapies. Emerging evidence demonstrates that GBM tumors are initiated from GSCs. The development and use of novel therapies including small molecule inhibitors of specific proteins in signaling pathways that regulate stemness, proliferation and migration of GSCs, immunotherapy, and non-coding microRNAs may provide better means of treating GBM. Identification and characterization of GSC-specific signaling pathways would be necessary to identify specific therapeutic targets which may lead to the development of more efficient therapies selectively targeting GSCs. Several signaling pathways including mTOR, AKT, maternal embryonic leucine zipper kinase (MELK), NOTCH1 and Wnt/β-catenin as well as expression of cancer stem cell markers CD133, CD44, Oct4, Sox2, Nanog, and ALDH1A1 maintain GSC properties. Moreover, the data published in the Cancer Genome Atlas (TCGA) specifically demonstrated the activated PI3K/AKT/mTOR pathway in GBM tumorigenesis. Studying such pathways may help to understand GSC biology and lead to the development of potential therapeutic interventions to render them more sensitive to chemotherapy and radiation therapy. Furthemore, recent demonstration of dedifferentiation of GBM cell lines into CSC-like cells prove that any successful therapeutic agent or combination of drugs for GBM therapy must eliminate not only GSCs, but the differentiated GBM cells and the entire bulk of tumor cells

Glioblastoma stem cells (GSCs) epigenetic plasticity and interconversion between differentiated non-GSCs and GSCs

AbstractCancer stem cells (CSCs) or cancer initiating cells (CICs) maintain self-renewal and multilineage differentiation properties of various tumors, as well as the cellular heterogeneity consisting of several subpopulations within tumors. CSCs display the malignant phenotype, self-renewal ability, altered genomic stability, specific epigenetic signature, and most of the time can be phenotyped by cell surface markers (e.g., CD133, CD24, and CD44). Numerous studies support the concept that non-stem cancer cells (non-CSCs) are sensitive to cancer therapy while CSCs are relatively resistant to treatment. In glioblastoma stem cells (GSCs), there is clonal heterogeneity at the genetic level with distinct tumorigenic potential, and defined GSC marker expression resulting from clonal evolution which is likely to influence disease progression and response to treatment. Another level of complexity in glioblastoma multiforme (GBM) tumors is the dynamic equilibrium between GSCs and differentiated non-GSCs, and the potential for non-GSCs to revert (dedifferentiate) to GSCs due to epigenetic alteration which confers phenotypic plasticity to the tumor cell population. Moreover, exposure of the differentiated GBM cells to therapeutic doses of temozolomide (TMZ) or ionizing radiation (IR) increases the GSC pool both in vitro and in vivo. This review describes various subtypes of GBM, discusses the evolution of CSC models and epigenetic plasticity, as well as interconversion between GSCs and differentiated non-GSCs, and offers strategies to potentially eliminate GSCs

WALDATA : Wavelet transform based adversarial learning for the detection of anomalous trading activities

Detecting manipulative activities in stock market trading poses a significant challenge due to the complex temporal correlations inherent to the dynamically changing stock price data. This challenge is further exacerbated by the limited availability of labelled anomalous trading data instances. Stock price manipulations, which consist of infrequent anomalies in stock price trading data, are challenging to capture due to their sporadic occurrence and dynamically evolving nature. This scarcity and inherent complexity significantly complicate the creation of labelled datasets hence hinders the development of robust detection of different stock price manipulation schemes through supervised learning methods. Overcoming these challenges is crucial for enhancing our understanding of market dynamics and implementing robust market surveillance systems. To address these challenges, we introduce a novel stock price manipulation detection approach called WALDATA (Wavelet Transform based Adversarial Learning for the Detection of Anomalous Trading Activities). We leverage the Wavelet Transform (WT) to decompose non-stationary stock price time series into informative features and capture multi-scale dynamics within the data. We encode stock price data by transforming it into scalogram images through the Continuous Wavelet Transform, effectively converting stock price time series data into a 2D image representation. Subsequently, we employ a Generative Adversarial Network (GAN) architecture, originally applied to computer vision, to learn the underlying distribution of normal trading behaviour from the encoded images. We then train the discriminator as an anomaly detector for identifying manipulative trading activities in the stock market. The efficacy of WALDATA is rigorously evaluated on diverse real-world stock datasets using 1-level tick data from the LOBSTER project and the experimental results demonstrate the significant performance of our approach achieving an average AUC of 0.99 while maintaining low false alarm rates across various market conditions. These findings not only validate the effectiveness of the proposed WALDATA approach in accurately identifying stock price manipulations but also provide investors and regulators alike with valuable insights for the development of advanced market surveillance systems. This research demonstrates the promising potential of combining wavelet-based feature extraction and stock price time series to image representation with generative adversarial learning frameworks for anomaly detection in financial time series data. The successful implementation of WALDATA contributes to the development of advanced market surveillance systems and paves the way for further advancements in market surveillance, contributing towards a more efficient and robust financial system and a fair market environment

Towards improved socio-economic assessments of ocean acidification’s impacts

Ocean acidification is increasingly recognized as a component of global change that could have a wide range of impacts on marine organisms, the ecosystems they live in, and the goods and services they provide humankind. Assessment of these potential socio-economic impacts requires integrated efforts between biologists, chemists, oceanographers, economists and social scientists. But because ocean acidification is a new research area, significant knowledge gaps are preventing economists from estimating its welfare impacts. For instance, economic data on the impact of ocean acidification on significant markets such as fisheries, aquaculture and tourism are very limited (if not non-existent), and non-market valuation studies on this topic are not yet available. Our paper summarizes the current understanding of future OA impacts and sets out what further information is required for economists to assess socio-economic impacts of ocean acidification. Our aim is to provide clear directions for multidisciplinary collaborative research

CHEOPS: The ESA Mission for Exo-Planets Characterization Ready for Launch

The European Space Agency (ESA) Science Programme Committee (SPC) selected CHEOPS (Characterizing Exoplanets Satellite) in October 2012 as the first Small-class mission (S1) within the Agency’s Scientific Programme. It is considered as a pilot case for implementing “small science missions” in the agency with the following requirements: science driven mission selected through an open Call; an implementation cycle, from the Call to launch, drastically shorter than for Medium-class (M) and Large-class (L) missions; a strict cost-cap to ESA, with possibly higher Member States involvement than for M or L missions. The CHEOPS mission is devoted to the characterization of known exoplanets orbiting bright stars, achieved through the precise measurement of exoplanet radii using the technique of transit photometry. It was adopted for implementation in February 2014 as a partnership between the ESA Science Programme and Switzerland, with a number of other Member States delivering significant contributions to the instrument development and to operations. The CHEOPS instrument is an optical Ritchey-Chrétien telescope with 300 mm effective aperture diameter and a large external baffle to minimize straylight. The compact CHEOPS spacecraft (approx. 300 kg, 1.5 m size), based on a flight-proven platform, will orbit the Earth in a dawn-dusk Sun Synchronous Orbit at 700 km altitude. CHEOPS completed the Preliminary Design Review at the end of September 2014, and passed the Critical Design Review in May 2016. In the course of 2017, flight platform and payload have been integrated and tested, and then followed by satellite level activities, targeting flight readiness by the end of year 2019. Implementation and validation of the ground segment, which is composed of the MOC (Mission Operations Centre), located in Torrejón (Madrid, Spain) and the SOC (Science Operations Centre), located at the University of Geneva (Switzerland) was achieved in parallel. CHEOPS will be launched as a secondary passenger on a Soyuz from Kourou by end of 2019. The paper describes the latest CHEOPS development status, focusing on the activities for verification and validation of the satellite and the system at large, including the ground segment and the activities in preparation for S/C launch and its operations. Additional details can be found on the ESA and UBE websites referred in [8]

On the computational derivation of bond-based peridynamic stress tensor

The concept of ‘contact stress’, as introduced by Cauchy, is a special case of a nonlocal stress tensor. In this work, the nonlocal stress tensor is derived through implementation of the bond-based formulation of peridynamics that uses an idealised model of interaction between points as bonds. The method is sufficiently general and can be implemented to study stress states in problems containing stress concentration, singularity, or discontinuities. Two case studies are presented, to study stress concentration around a circular hole in a square plate and conventionally singular stress fields in the vicinity of a sharp crack tip. The peridynamic stress tensor is compared with finite element approximations and available analytical solutions. It is shown that peridynamics is capable of capturing both shear and direct stresses and the results obtained correlate well with those obtained using analytical solutions and finite element approximations. A built-in MATLAB code is developed and used to construct a 2D peridynamic grid and subsequently approximate the solution of the peridynamic equation of motion. The stress tensor is then obtained using the tensorial product of bond force projections for bonds that geometrically pass through the point. To evaluate the accuracy of the predicted stresses near a crack tip, the J-integral value is computed using both a direct contour approximation and the equivalent domain integral method. In the formulation of the contour approximation, bond forces are used directly while the proposed peridynamic stress tensor is used for the domain method. The J-integral values computed are compared with those obtained by the commercial finite element package Abaqus 2018. The comparison provides an indication on the accurate prediction of the state of stress near the crack tip

Boundary Conditions for Elastohydrodynamics of Circular Point Contacts

The paper presents the solution of an elastohydrodynamic point contact condition using inlet and outlet lubricant entrainment with partial counter-flow. The inlet and outlet boundaries are determined using potential flow analysis for the pure rolling of contiguous surfaces. This shows that Swift–Stieber boundary conditions best conform to the observed partial counter-flow at the inlet conjunction, satisfying the compatibility condition. For the outlet region, the same is true when Prandtl–Hopkins boundary conditions are employed. Using these boundary conditions, the predictions conform closely to the measured pressure distribution using a deposited pressure-sensitive micro-transducer in a ball-to-flat race contact. Furthermore, the predicted conjunctional shape closely conforms to the often observed characteristic keyhole conjunction through optical interferometry. The combined numerical–experimental analysis with realistic boundary conditions described here has not hitherto been reported in the literature