Improved Hardness of Approximation for Stackelberg Shortest-Path Pricing

We consider the Stackelberg shortest-path pricing problem, which is defined as follows. Given a graph G with fixed-cost and pricable edges and two distinct vertices s and t, we may assign prices to the pricable edges. Based on the predefined fixed costs and our prices, a customer purchases a cheapest s-t-path in G and we receive payment equal to the sum of prices of pricable edges belonging to the path. Our goal is to find prices maximizing the payment received from the customer. While Stackelberg shortest-path pricing was known to be APX-hard before, we provide the first explicit approximation threshold and prove hardness of approximation within 2−o(1). We also argue that the nicely structured type of instance resulting from our reduction captures most of the challenges we face in dealing with the problem in general and, in particular, we show that the gap between the revenue of an optimal pricing and the only known general upper bound can still be logarithmically large

Assortment optimisation under a general discrete choice model: A tight analysis of revenue-ordered assortments

The assortment problem in revenue management is the problem of deciding which subset of products to offer to consumers in order to maximise revenue. A simple and natural strategy is to select the best assortment out of all those that are constructed by fixing a threshold revenue $\pi$ and then choosing all products with revenue at least $\pi$. This is known as the revenue-ordered assortments strategy. In this paper we study the approximation guarantees provided by revenue-ordered assortments when customers are rational in the following sense: the probability of selecting a specific product from the set being offered cannot increase if the set is enlarged. This rationality assumption, known as regularity, is satisfied by almost all discrete choice models considered in the revenue management and choice theory literature, and in particular by random utility models. The bounds we obtain are tight and improve on recent results in that direction, such as for the Mixed Multinomial Logit model by Rusmevichientong et al. (2014). An appealing feature of our analysis is its simplicity, as it relies only on the regularity condition. We also draw a connection between assortment optimisation and two pricing problems called unit demand envy-free pricing and Stackelberg minimum spanning tree: These problems can be restated as assortment problems under discrete choice models satisfying the regularity condition, and moreover revenue-ordered assortments correspond then to the well-studied uniform pricing heuristic. When specialised to that setting, the general bounds we establish for revenue-ordered assortments match and unify the best known results on uniform pricing.Comment: Minor changes following referees' comment

The selective PI3Kα inhibitor BYL719 as a novel therapeutic option for neuroendocrine tumors: Results from multiple cell line models

BACKGROUND/AIMS The therapeutic options for metastatic neuroendocrine tumors (NETs) are limited. As PI3K signaling is often activated in NETs, we have assessed the effects of selective PI3Kp110\textgreeka inhibition by the novel agent BYL719 on cell viability, colony formation, apoptosis, cell cycle, signaling pathways, differentiation and secretion in pancreatic (BON-1, QGP-1) and pulmonary (H727) NET cell lines. METHODS Cell viability was investigated by WST-1 assay, colony formation by clonogenic assay, apoptosis by caspase3/7 assay, the cell cycle by FACS, cell signaling by Western blot analysis, expression of chromogranin A and somatostatin receptors 1/2/5 by RT-qPCR, and chromogranin A secretion by ELISA. RESULTS BYL719 dose-dependently decreased cell viability and colony formation with the highest sensitivity in BON-1, followed by H727, and lowest sensitivity in QGP-1 cells. BYL719 induced apoptosis and G0/G1 cell cycle arrest associated with increased p27 expression. Western blots showed inhibition of PI3K downstream targets to a varying degree in the different cell lines, but IGF1R activation. The most sensitive BON-1 cells displayed a significant, and H727 cells a non-significant, GSK3 inhibition after BYL719 treatment, but these effects do not appear to be mediated through the IGF1R. In contrast, the most resistant QGP-1 cells showed no GSK3 inhibition, but a modest activation, which would partially counteract the other anti-proliferative effects. Accordingly, BYL719 enhanced neuroendocrine differentiation with the strongest effect in BON-1, followed by H727 cells indicated by induction of chromogranin A and somatostatin receptor 1/2 mRNA-synthesis, but not in QGP-1 cells. In BON-1 and QGP-1 cells, the BYL719/everolimus combination was synergistic through simultaneous AKT/mTORC1 inhibition, and significantly increased somatostatin receptor 2 transcription compared to each drug separately. CONCLUSION Our results suggest that the agent BYL719 could be a novel therapeutic approach to the treatment of NETs that may sensitize NET cells to somatostatin analogs, and that if there is resistance to its action this may be overcome by combination with everolimus

Combinatorial Auctions without Money

Algorithmic Mechanism Design attempts to marry computation and incentives, mainly by leveraging monetary transfers between designer and selfish agents involved. This is principally because in absence of money, very little can be done to enforce truthfulness. However, in certain applications, money is unavailable, morally unacceptable or might simply be at odds with the objective of the mechanism. For example, in Combinatorial Auctions (CAs), the paradigmatic problem of the area, we aim at solutions of maximum social welfare, but still charge the society to ensure truthfulness. We focus on the design of incentive-compatible CAs without money in the general setting of k-minded bidders. We trade monetary transfers with the observation that the mechanism can detect certain lies of the bidders: i.e., we study truthful CAs with verification and without money. In this setting, we characterize the class of truthful mechanisms and give a host of upper and lower bounds on the approximation ratio obtained by either deterministic or randomized truthful mechanisms. Our results provide an almost complete picture of truthfully approximating CAs in this general setting with multi-dimensional bidders

Does the proteasome inhibitor bortezomib sensitize to DNA-damaging therapy in gastroenteropancreatic neuroendocrine neoplasms? - A preclinical assessment in vitro and in vivo

BACKGROUND: Well-differentiated gastroenteropancreatic neuroendocrine neoplasms are rare tumors with a slow proliferation. They are virtually resistant to many DNA-damaging therapeutic approaches, such as chemo- and external beam therapy, which might be overcome by DNA damage inhibition induced by proteasome inhibitors such as bortezomib. METHODS AND RESULTS: In this study, we assessed several combined treatment modalities in vitro and in vivo. By cell-based functional analyses, in a 3D in ovo and an orthotopic mouse model, we demonstrated sensitizing effects of bortezomib combined with cisplatin, radiation and peptide receptor radionuclide therapy (PRRT). By gene expression profiling and western blot, we explored the underlying mechanisms, which resulted in an impaired DNA damage repair. Therapy-induced DNA damage triggered extrinsic proapoptotic signaling as well as the induction of cell cycle arrest, leading to a decreased vital tumor volume and altered tissue composition shown by magnetic resonance imaging and F-18-FDG-PET in vivo, however with no significant additional benefit related to PRRT alone. CONCLUSIONS: We demonstrated that bortezomib has short-term sensitizing effects when combined with DNA damaging therapy by interfering with DNA repair in vitro and in ovo. Nevertheless, due to high tumor heterogeneity after PRRT in long-term observations, we were not able to prove a therapeutic advantage of bortezomib-combined PRRT in an in vivo mouse model

Does the proteasome inhibitor bortezomib sensitize to DNA-damaging therapy in gastroenteropancreatic neuroendocrine neoplasms? – A preclinical assessment in vitro and in vivo

Background: Well-differentiated gastroenteropancreatic neuroendocrine neoplasms are rare tumors with a slow proliferation. They are virtually resistant to many DNA-damaging therapeutic approaches, such as chemo- and external beam therapy, which might be overcome by DNA damage inhibition induced by proteasome inhibitors suc

Frequent ZNF217 mutations lead to transcriptional deregulation of interferon signal transduction via altered chromatin accessibility in B cell lymphoma

Recent exome-wide studies discovered frequent somatic mutations in the epigenetic modifier ZNF217 in primary mediastinal B cell lymphoma (PMBCL) and related disorders. As functional consequences of ZNF217 alterations remain unknown, we comprehensively evaluated their impact in PMBCL. Targeted sequencing identified genetic lesions affecting ZNF217 in 33% of 157 PMBCL patients. Subsequent gene expression profiling (n = 120) revealed changes in cytokine and interferon signal transduction in ZNF217-aberrant PMBCL cases. In vitro, knockout of ZNF217 led to changes in chromatin accessibility interfering with binding motifs for crucial lymphoma-associated transcription factors. This led to disturbed expression of interferon-responsive and inflammation-associated genes, altered cell behavior, and aberrant differentiation. Mass spectrometry demonstrates that ZNF217 acts within a histone modifier complex containing LSD1, CoREST and HDAC and interferes with H3K4 methylation and H3K27 acetylation. Concluding, our data suggest non-catalytic activity of ZNF217, which directs histone modifier complex function and controls B cell differentiation-associated patterns of chromatin structure

Experimental supplements to the computational complexity analysis of genetic programming for problems modelling isolated program semantics

In this paper, we carry out experimental investigations that complement recent theoretical investigations on the runtime of simple genetic programming algorithms [3, 7]. Crucial measures in these theoretical analyses are the maximum tree size that is attained during the run of the algorithms as well as the population size when dealing with multi-objective models. We study those measures in detail by experimental investigations and analyze the runtime of the different algorithms in an experimental way.Tommaso Urli, Markus Wagner and Frank Neuman