Why one-size-fits-all vaso-modulatory interventions fail to control glioma invasion: in silico insights

There is an ongoing debate on the therapeutic potential of vaso-modulatory interventions against glioma invasion. Prominent vasculature-targeting therapies involve functional tumour-associated blood vessel deterioration and normalisation. The former aims at tumour infarction and nutrient deprivation medi- ated by vascular targeting agents that induce occlusion/collapse of tumour blood vessels. In contrast, the therapeutic intention of normalising the abnormal structure and function of tumour vascular net- works, e.g. via alleviating stress-induced vaso-occlusion, is to improve chemo-, immuno- and radiation therapy efficacy. Although both strategies have shown therapeutic potential, it remains unclear why they often fail to control glioma invasion into the surrounding healthy brain tissue. To shed light on this issue, we propose a mathematical model of glioma invasion focusing on the interplay between the mi- gration/proliferation dichotomy (Go-or-Grow) of glioma cells and modulations of the functional tumour vasculature. Vaso-modulatory interventions are modelled by varying the degree of vaso-occlusion. We discovered the existence of a critical cell proliferation/diffusion ratio that separates glioma invasion re- sponses to vaso-modulatory interventions into two distinct regimes. While for tumours, belonging to one regime, vascular modulations reduce the tumour front speed and increase the infiltration width, for those in the other regime the invasion speed increases and infiltration width decreases. We show how these in silico findings can be used to guide individualised approaches of vaso-modulatory treatment strategies and thereby improve success rates

Quantum effects in a rotating spacetime

The behavior of a arbitrary coupled quantum scalar field is studied in the background of the G\"odel spacetime. Closed forms are derived for the effective action and the vacuum expectation value of quadratic field fluctuations by using $\zeta$-function regularization. Based on these results, we argue that causality violation presented in this spacetime can not be removed by quantum effects.Comment: 17 pages, LaTe

Co-Evaluation of Pattern Matching Algorithms on IoT Devices with Embedded GPUs

Pattern matching is an important building block for many security applications, including Network Intrusion Detection Systems (NIDS). As NIDS grow in functionality and complexity, the time overhead and energy consumption of pattern matching become a significant consideration that limits the deployability of such systems, especially on resource-constrained devices.\ua0On the other hand, the emergence of new computing platforms, such as embedded devices with integrated, general-purpose Graphics Processing Units (GPUs), brings new, interesting challenges and opportunities for algorithm design in this setting: how to make use of new architectural features and how to evaluate their effect on algorithm performance. Up to now, work that focuses on pattern matching for such platforms has been limited to specific algorithms in isolation.In this work, we present a systematic and comprehensive benchmark that allows us to co-evaluate both existing and new pattern matching algorithms on heterogeneous devices equipped with embedded GPUs, suitable for medium- to high-level IoT deployments. We evaluate the algorithms on such a heterogeneous device, in close connection with the architectural features of the platform and provide insights on how these features affect the algorithms\u27 behavior. We find that, in our target embedded platform, GPU-based pattern matching algorithms have competitive performance compared to the CPU and consume half as much energy as the CPU-based variants.\ua0Based on these insights, we also propose HYBRID, a new pattern matching approach that efficiently combines techniques from existing approaches and outperforms them by 1.4x, across a range of realistic and synthetic data sets. Our benchmark details the effect of various optimizations, thus providing a path forward to make existing security mechanisms such as NIDS deployable on IoT devices

Finite Number and Finite Size Effects in Relativistic Bose-Einstein Condensation

Bose-Einstein condensation of a relativistic ideal Bose gas in a rectangular cavity is studied. Finite size corrections to the critical temperature are obtained by the heat kernel method. Using zeta-function regularization of one-loop effective potential, lower dimensional critical temperatures are calculated. In the presence of strong anisotropy, the condensation is shown to occur in multisteps. The criteria of this behavior is that critical temperatures corresponding to lower dimensional systems are smaller than the three dimensional critical temperature.Comment: 18 pages, 9 figures, Fig.3 replaced, to appear in Physical Review

O(N) Quantum fields in curved spacetime

For the O(N) field theory with lambda Phi^4 self-coupling, we construct the two-particle-irreducible (2PI), closed-time-path (CTP) effective action in a general curved spacetime. From this we derive a set of coupled equations for the mean field and the variance. They are useful for studying the nonperturbative, nonequilibrium dynamics of a quantum field when full back reactions of the quantum field on the curved spacetime, as well as the fluctuations on the mean field, are required. Applications to phase transitions in the early Universe such as the Planck scale or in the reheating phase of chaotic inflation are under investigation.Comment: 31 pages, 2 figures, uses RevTeX 3.1, LaTeX 2e, AMSfonts 2.2, graphics 0.6; To appear in Phys. Rev. D (7/15/97

Angiotensin inhibition enhances drug delivery and potentiates chemotherapy by decompressing tumour blood vessels

Cancer and stromal cells actively exert physical forces (solid stress) to compress tumour blood vessels, thus reducing vascular perfusion. Tumour interstitial matrix also contributes to solid stress, with hyaluronan implicated as the primary matrix molecule responsible for vessel compression because of its swelling behaviour. Here we show, unexpectedly, that hyaluronan compresses vessels only in collagen-rich tumours, suggesting that collagen and hyaluronan together are critical targets for decompressing tumour vessels. We demonstrate that the angiotensin inhibitor losartan reduces stromal collagen and hyaluronan production, associated with decreased expression of profibrotic signals TGF-β1, CCN2 and ET-1, downstream of angiotensin-II-receptor-1 inhibition. Consequently, losartan reduces solid stress in tumours resulting in increased vascular perfusion. Through this physical mechanism, losartan improves drug and oxygen delivery to tumours, thereby potentiating chemotherapy and reducing hypoxia in breast and pancreatic cancer models. Thus, angiotensin inhibitors—inexpensive drugs with decades of safe use—could be rapidly repurposed as cancer therapeutics.National Cancer Institute (U.S.) (Grant P01-CA080124)National Cancer Institute (U.S.) (Grant R01-CA126642)National Cancer Institute (U.S.) (Grant R01-CA085140)National Cancer Institute (U.S.) (Grant R01-CA115767)National Cancer Institute (U.S.) (Grant R01-CA098706)United States. Dept. of Defense. Breast Cancer Research Program (Innovator Award W81XWH-10-1-0016)Lustgarten Foundation (Dana-Farber Cancer Institute/David H. Koch Institute for Integrative Cancer Research at MIT Bridge Project Grant

Intrusion Detection in Industrial Networks via Data Streaming

Given the increasing threat surface of industrial networks due to distributed, Internet-of-Things (IoT) based system architectures, detecting intrusions in\ua0 Industrial IoT (IIoT) systems is all the more important, due to the safety implications of potential threats. The continuously generated data in such systems form both a challenge but also a possibility: data volumes/rates are high and require processing and communication capacity but they contain information useful for system operation and for detection of unwanted situations.In this chapter we explain that\ua0 stream processing (a.k.a. data streaming) is an emerging useful approach both for general applications and for intrusion detection in particular, especially since it can enable data analysis to be carried out in the continuum of edge-fog-cloud distributed architectures of industrial networks, thus reducing communication latency and gradually filtering and aggregating data volumes. We argue that usefulness stems also due to\ua0 facilitating provisioning of agile responses, i.e. due to potentially smaller latency for intrusion detection and hence also improved possibilities for intrusion mitigation. In the chapter we outline architectural features of IIoT networks, potential threats and examples of state-of-the art intrusion detection methodologies. Moreover, we give an overview of how leveraging distributed and parallel execution of streaming applications in industrial setups can influence the possibilities of protecting these systems. In these contexts, we give examples using electricity networks (a.k.a. Smart Grid systems).We conclude that future industrial networks, especially their Intrusion Detection Systems (IDSs), should take advantage of data streaming concept by decoupling semantics from the deployment

Micromechanical study of the load transfer in a polycaprolactone-collagen hybrid scaffold when subjected to unconfined and confined compression

Scaffolds are used in diverse tissue engineering applications as hosts for cell proliferation and extracellular matrix formation. One of the most used tissue engineering materials is collagen, which is well known to be a natural biomaterial, also frequently used as cell substrate, given its natural abundance and intrinsic biocompatibility. This study aims to evaluate how the macroscopic biomechanical stimuli applied on a construct made of polycaprolactone scaffold embedded in a collagen substrate translate into microscopic stimuli at the cell level. Eight poro-hyperelastic finite element models of 3D printed hybrid scaffolds from the same batch were created, along with an equivalent model of the idealized geometry of that scaffold. When applying an 8% confined compression at the macroscopic level, local fluid flow of up to 20 [Formula: see text]m/s and octahedral strain levels mostly under 20% were calculated in the collagen substrate. Conversely unconfined compression induced fluid flow of up to 10 [Formula: see text]m/s and octahedral strain from 10 to 35%. No relevant differences were found amongst the scaffold-specific models. Following the mechanoregulation theory based on Prendergast et al. (J Biomech 30:539-548, 1997. https://doi.org/10.1016/S0021-9290(96)00140-6 ), those results suggest that mainly cartilage or fibrous tissue formation would be expected to occur under unconfined or confined compression, respectively. This in silico study helps to quantify the microscopic stimuli that are present within the collagen substrate and that will affect cell response under in vitro bioreactor mechanical stimulation or even after implantation

Layer-by-Layer Nanoparticles for Systemic Codelivery of an Anticancer Drug and siRNA for Potential Triple-Negative Breast Cancer Treatment

A single nanoparticle platform has been developed through the modular and controlled layer-by-layer process to codeliver siRNA that knocks down a drug-resistance pathway in tumor cells and a chemotherapy drug to challenge a highly aggressive form of triple-negative breast cancer. Layer-by-layer films were formed on nanoparticles by alternately depositing siRNA and poly-l-arginine; a single bilayer on the nanoparticle surface could effectively load up to 3500 siRNA molecules, and the resulting LbL nanoparticles exhibit an extended serum half-life of 28 h. In animal models, one dose via intravenous administration significantly reduced the target gene expression in the tumors by almost 80%. By generating the siRNA-loaded film atop a doxorubicin-loaded liposome, we identified an effective combination therapy with siRNA targeting multidrug resistance protein 1, which significantly enhanced doxorubicin efficacy by 4 fold in vitro and led to up to an 8-fold decrease in tumor volume compared to the control treatments with no observed toxicity. The results indicate that the use of layer-by-layer films to modify a simple liposomal doxorubicin delivery construct with a synergistic siRNA can lead to significant tumor reduction in the cancers that are otherwise nonresponsive to treatment with Doxil or other common chemotherapy drugs. This approach provides a potential strategy to treat aggressive and resistant cancers, and a modular platform for a broad range of controlled multidrug therapies customizable to the cancer type in a singular nanoparticle delivery system.Janssen Pharmaceutical Ltd. (TRANSCEND Grant)National Cancer Institute (U.S.) (Koch Institute Support (Core) Grant P30-CA14051)National Health and Medical Research Council (Australia) (CJ Martin Fellowship)National Science Foundation (U.S.). Graduate Research FellowshipNatural Sciences and Engineering Research Council of Canada (Postdoctoral Fellowship