Stromal cell effects on melanoma cell drug response

Thesis (M.A.)--Boston UniversityObjective: Melanoma is currently one of the deadliest forms of skin disease in the United States. However in the past decade there have been significant advances in treatment. Among the most promising recent developments, inhibitors of the serine/threonine-protein kinase B-Raf (BRAF inhibitors) such as vemurafenib show great promise and have been shown to increase the median survival of patients with melanoma cells that harbor a mutation of the BRAF gene. While BRAF inhibitors and other treatment therapies have much potential, more needs to be done to improve treatment. As with other cancers, a major hurdle in the treatment of melanoma is the eventual tumor resistance to drug therapy. Accessory cells are thought to play a large role in mediating tumor resistance to drug treatment. Stromal cells have been known to release cytokines and growth factors that aid in cancer proliferation. They can also expression adhesion molecules that further help to aid cell growth and tumor development. It has also been demonstrated that these accessory cells can significantly alter cancer cell drug response as a result of the factors they release or express on their surface. In this study we hypothesize that certain anti-cancer drugs will behave differently against melanoma cell line A375 in the presence versus the absence of stromal cells. Methods: Melanoma cell line A375 was grown on 384 well plates in the presence or absence of different stromal cell lines. A number of different drugs were screened using Compartment-Specific Bioluminescence Imaging to determine if there was a difference in A375 proliferation after drug treatment in the presence versus absence of accessory cells. After an initial screen, a few drugs were chosen to generate dose-response curves to determine if different drugs had different effects at various doses in the presence or absence of stromal cells. [TRUNCATED

Constructing Consumer Sentiment Index for U.S. Using Google Searches

We construct a consumer sentiment index for the U.S. using the popularity trends of selected Google searches. The final index consists of four components and is highly correlated with the Index of Consumer Sentiment from the University of Michigan and the Consumer Confidence Index from the Conference Board. Among the three sentiment indices, the Google search-based index (SBI) leads in time and predicts other indices. In terms of forecasting consumer spending, the SBI outperforms both the ICS and the CCI and provides independent information. For robustness, we use multiple measures of consumer spending and a range of statistical specifications. The finding is robust.consumer sentiment; consumer confidence; leading economic indicators

The impact of the Geometric Correction Scheme on MEG functional topology at rest

Spontaneous activity is correlated across brain regions in large scale networks (RSN) closely resembling those recruited during several behavioral tasks and characterized by functional specialization and dynamic integration. Specifically, MEG studies revealed a set of central regions (dynamic core) possibly facilitating communication among differently specialized brain systems. However, source projected MEG signals, due to the fundamentally ill-posed inverse problem, are affected by spatial leakage, leading to the estimation of spurious, blurred connections that may affect the topological properties of brain networks and their integration. To reduce leakage effects, several correction schemes have been proposed including the Geometric Correction Scheme (GCS) whose theory, simulations and empirical results on topography of a few RSNs were already presented. However, its impact on the estimation of fundamental graph measures used to describe the architecture of interactions among brain regions has not been investigated yet. Here, we estimated dense, MEG band-limited power connectomes in theta, alpha, beta, and gamma bands from 13 healthy subjects (all young adults). We compared the connectivity and topology of MEG uncorrected and GCS-corrected connectomes. The use of GCS considerably reorganized the topology of connectivity, reducing the local, within-hemisphere interactions mainly in the beta and gamma bands and increasing across-hemisphere interactions mainly in the alpha and beta bands. Moreover, the number of hubs decreased in the alpha and beta bands, but the centrality of some fundamental regions such as the Posterior Cingulate Cortex (PCC), Supplementary Motor Area (SMA) and Middle Prefrontal Cortex (MPFC) remained strong in all bands, associated to an increase of the Global Efficiency and a decrease of Modularity. As a comparison, we applied orthogonalization on connectomes and ran the same topological analyses. The correlation values were considerably reduced, and orthogonalization mainly decreased local within-hemisphere interactions in all bands, similarly to GCS. Notably, the centrality of the PCC, SMA and MPFC was preserved in all bands, as for GCS, together with other hubs in the posterior parietal regions. Overall, leakage correction removes spurious local connections, but confirms the role of dynamic hub regions, specifically the anterior and posterior cingulate, in integrating information in the brain at rest

Developing twenty-first century skills: insights from an intensive interdisciplinary workshop Mosaic of Life

The Baltic Sea, one of the world’s largest semi-enclosed seas, which, with its very low salinity and quasi-isolation from the big oceans cannot decide whether it is a sea or a large lake. This geologically-unique environment supports an even more surprising and delicate marine ecosystem, where a complex community of fishes, marine mammals and important microscopic organisms creates a magical mosaic of life. Humans have enjoyed the abundance of life in the Baltic Sea for thousands of years, and major Scandinavian and Baltic cities have oriented themselves towards this geo-ecosystem in order to develop and seek ecological, economical and cultural inspiration and wealth. The ‘Mosaic of Life’ workshop aimed at going beyond the obvious in examining the meaning of the Baltic Sea by gathering together a selection of young, creative minds from different backgrounds ranging from the arts and economics to geology and life sciences. This intensive workshop was designed as a unique training opportunity to develop essential twenty-first century skills – to introduce and develop creative, critical and interdisciplinary thinking and collaborative teamwork, as well as to foster a visual and scientific literacy, using project-based learning and hands-on activities. Our final goal has been to be inspired by the resulting connections, differences and unifying concepts, creating innovative, interdisciplinary projects which would look further than the sea – further than the eye can see and further into the future

The Cost of Sybils, Credible Commitments, and False-Name Proof Mechanisms

Consider a mechanism that cannot observe how many players there are directly, but instead must rely on their self-reports to know how many are participating. Suppose the players can create new identities to report to the auctioneer at some cost $c$. The usual mechanism design paradigm is equivalent to implicitly assuming that $c$ is infinity for all players, while the usual Sybil attacks literature is that it is zero or finite for one player (the attacker) and infinity for everyone else (the 'honest' players). The false-name proof literature largely assumes the cost to be 0. We consider a model with variable costs that unifies these disparate streams. A paradigmatic normal form game can be extended into a Sybil game by having the action space by the product of the feasible set of identities to create action where each player chooses how many players to present as in the game and their actions in the original normal form game. A mechanism is (dominant) false-name proof if it is (dominant) incentive-compatible for all the players to self-report as at most one identity. We study mechanisms proposed in the literature motivated by settings where anonymity and self-identification are the norms, and show conditions under which they are not Sybil-proof. We characterize a class of dominant Sybil-proof mechanisms for reward sharing and show that they achieve the efficiency upper bound. We consider the extension when agents can credibly commit to the strategy of their sybils and show how this can break mechanisms that would otherwise be false-name proof

Towards Optimal Prior-Free Permissionless Rebate Mechanisms, with applications to Automated Market Makers & Combinatorial Orderflow Auctions

Maximal Extractable Value (MEV) has become a critical issue for blockchain ecosystems, as it enables validators or block proposers to extract value by ordering, including or censoring users' transactions. This paper aims to present a formal approach for determining the appropriate compensation for users whose transactions are executed in bundles, as opposed to individually. We explore the impact of MEV on users, discuss the Shapley value as a solution for fair compensation, and delve into the mechanisms of MEV rebates and auctions as a means to undermine the power of the block producer

Automated Planning for Urban Traffic Control: Strategic Vehicle Routing to Respect Air Quality Limitations

The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. These trends are occurring in the context of concerns around environmental issues of poor air quality and transport related carbon dioxide emissions. One out of several ways to help meet these challenges is in the intelligent routing of road traffic through congested urban areas. Our goal is to show the feasibility of using automated planning to perform this routing, taking into account a knowledge of vehicle types, vehicle emissions, route maps, air quality zones, etc. Specifically focusing on air quality concerns, in this paper we investigate the problem where the goals are to minimise overall vehicle delay while utilising network capacity fully, and respecting air quality limits. We introduce an automated planning approach for the routing of traffic to address these areas. The approach has been evaluated on micro-simulation models that use real-world data supplied by our industrial partner. Results show the feasibility of using AI planning technology to deliver efficient routes for vehicles that avoid the breaking of air quality limits, and that balance traffic flow through the network