Instruments for patent data analyses in business firms

The availability of patent data in data banks has largely increased the possibilities for patent analyses. In this paper we discuss overall measures of patent activity for individual firms. As the data are broken down by technology fields we introduce patent portfolios that represent the patent position of a firm relative to its competitors over all technology fields covered. We then use matrix algebra to suggest an alternative to standard procedures of importance weightings for patents, and we extent the analysis to show the relations between inventors and end-products via patents that are used in the end-products. This may have many applications, from inventor remuneration to human resource management. The paper concludes with suggestions for further research

Issues in predicting the demand for a new technological product

This paper focuses on various issues that need to be considered when an analyst wishes to predict the demand for a new technological product. The interplay of the strategic behavior of the firm, notably with regard to preannouncing the future availability of the new product and the behavior of channel members and prospective customers is highlighted in this paper. Prospective customers' choice behavior is governed by such factors as credibility of the firm's preannouncement and expectations on the availability of the new product while a channel member's decision to accept the new product depends upon the expected sales and a demonstration of the product's acceptance by other channel members. These issues lead logically to the interdependence between the product positioning and the preannouncement decisions potential of the firm. The paper concludes with a discussion of the data requirements for implementing the approach suggested by the model and implications for product development process and competitive behavior

IL-15 enhances the anti-tumor activity of trastuzumab against breast cancer cells but causes fatal side effects in humanized tumor mice (HTM)

Cancer immunotherapy has been shown to enhance established treatment regimens. We evaluated the potential reinforcing effect of IL-15 in trastuzumab treated humanized tumor mice (HTM) which were generated by concurrent transplantation of neonatal NOD-scid IL2R.null mice with human hematopoietic stem cells (HSC) and HER2 positive breast cancer cells (metastasizing SK-BR-3, solid tumor forming BT474). We found that trastuzumab treatment efficacy mainly depends on the immediate anti-tumorigenic cellular effect which is significantly enhanced by tumor interacting immune cells upon cotransplantion of HSC. However, trastuzumab treatment caused elevated CD44 expression on tumor cells that metastasized into the lung and liver but did not hinder tumor cell dissemination into the bone marrow. Moreover, in a number of SK-BR-3-transplanted animals disseminated CD44(high)/CD24(low) tumor cells lost trastuzumab sensitivity. Concerning the FcYRIIIa polymorphism, trastuzumab treatment efficiency in HTM was higher in mice with NK-cells harboring the high affinity FcYRIIIa compared to those with low affinity Fc.RIIIa. In contrast, IL-15 caused the strongest NK-cell activation in heterozygous low affinity Fc.RIIIa animals. Although IL-15 enhanced the trastuzumab mediated tumor defense, an unspecific immune stimulation resulted in preterm animal death due to systemic inflammation. Overall, treatment studies based on "patient-like" HTM revealed critical and adverse immune-related mechanisms which must be managed prior to clinical testing

Using Comparative Preference Statements in Hypervolume-Based Interactive Multiobjective Optimization

International audienceThe objective functions in multiobjective optimization problems are often non-linear, noisy, or not available in a closed form and evolutionary multiobjective optimization (EMO) algorithms have been shown to be well applicable in this case. Here, our objective is to facilitate interactive decision making by saving function evaluations outside the "interesting" regions of the search space within a hypervolume-based EMO algorithm. We focus on a basic model where the Decision Maker (DM) is always asked to pick the most desirable solution among a set. In addition to the scenario where this solution is chosen directly, we present the alternative to specify preferences via a set of so-called comparative preference statements. Examples on standard test problems show the working principles, the competitiveness, and the drawbacks of the proposed algorithm in comparison with the recent iTDEA algorithm

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Proceedings of: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011.The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have a intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work we argue that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.This work was supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.Publicad

Responsible Innovation in Business

This chapter introduces responsible innovation in a business context. The first part explains the basic terms that constitute responsible innovation from a busi-ness perspective. The second part presents tangible business practices that opera-tionalise responsible innovation and introduces two good practice examples that hint at the variety of ways in which responsible innovation can be implemented in companies

Immune Checkpoint Profiling in Humanized Breast Cancer Mice Revealed Cell-Specific LAG-3/PD-1/TIM-3 Co-Expression and Elevated PD-1/TIM-3 Secretion

Checkpoint blockade is particularly based on PD-1/PD-L1-inhibiting antibodies. However, an efficient immunological tumor defense can be blocked not only by PD-(L)1 but also by the presence of additional immune checkpoint molecules. Here, we investigated the co-expression of several immune checkpoint proteins and the soluble forms thereof (e.g., PD-1, TIM-3, LAG-3, PD-L1, PD-L2 and others) in humanized tumor mice (HTM) simultaneously harboring cell line-derived (JIMT-1, MDA-MB-231, MCF-7) or patient-derived breast cancer and a functional human immune system. We identified tumor-infiltrating T cells with a triple-positive PD-1, LAG-3 and TIM-3 phenotype. While PD-1 expression was increased in both the CD4 and CD8 T cells, TIM-3 was found to be upregulated particularly in the cytotoxic T cells in the MDA-MB-231-based HTM model. High levels of soluble TIM-3 and galectin-9 (a TIM-3 ligand) were detected in the serum. Surprisingly, soluble PD-L2, but only low levels of sPD-L1, were found in mice harboring PD-L1-positive tumors. Analysis of a dataset containing 3039 primary breast cancer samples on the R2 Genomics Analysis Platform revealed increased TIM-3, galectin-9 and LAG-3 expression, not only in triple-negative breast cancer but also in the HER2+ and hormone receptor-positive breast cancer subtypes. These data indicate that LAG-3 and TIM-3 represent additional key molecules within the breast cancer anti-immunity landscape

Neoadjuvant radiotherapy in ER+, HER2+, and triple-negative -specific breast cancer based humanized tumor mice enhances anti-PD-L1 treatment efficacy

Pre-operative radiation therapy is not currently integrated into the treatment protocols for breast cancer. However, transforming immunological “cold” breast cancers by neoadjuvant irradiation into their “hot” variants is supposed to elicit an endogenous tumor immune defense and, thus, enhance immunotherapy efficiency. We investigated cellular and immunological effects of sub-lethal, neoadjuvant irradiation of ER pos., HER2 pos., and triple-negative breast cancer subtypes in-vitro and in-vivo in humanized tumor mice (HTM). This mouse model is characterized by a human-like immune system and therefore facilitates detailed analysis of the mechanisms and efficiency of neoadjuvant, irradiation-induced “in-situ vaccination”, especially in the context of concurrently applied checkpoint therapy. Similar to clinical appearances, we observed a gradually increased immunogenicity from the luminal over the HER2-pos. to the triple negative subtype in HTM indicated by an increasing immune cell infiltration into the tumor tissue. Anti-PD-L1 therapy divided the HER2-pos. and triple negative HTM groups into responder and non-responder, while the luminal HTMs were basically irresponsive. Irradiation alone was effective in the HER2-pos. and luminal subtype-specific HTM and was supportive for overcoming irresponsiveness to single anti-PD-L1 treatment. The treatment success correlated with a significantly increased T cell proportion and PD-1 expression in the spleen. In all subtype-specific HTM combination therapy proved most effective in diminishing tumor growth, enhancing the immune response, and converted non-responder into responder during anti-PD-L1 therapy. In HTM, neoadjuvant irradiation reinforced anti-PD-L1 checkpoint treatment of breast cancer in a subtype –specific manner. According to the “bench to bedside” principle, this study offers a vital foundation for clinical translating the use of neoadjuvant irradiation in the context of checkpoint therapy

Visualising Evolution History in Multi- and Many-Objective Optimisation

Evolutionary algorithms are widely used to solve optimisation problems. However, challenges of transparency arise in both visualising the processes of an optimiser operating through a problem and understanding the problem features produced from many-objective problems, where comprehending four or more spatial dimensions is difficult. This work considers the visualisation of a population as an optimisation process executes. We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape. This is particularly valuable if the problem landscape is unknown, contains unknown features or is a many-objective problem. We have shown how using this framework is effective on a suite of multi- and many-objective benchmark test problems, optimising them with NSGA-II and NSGA-III

Differential Expression of PD-L1 during Cell Cycle Progression of Head and Neck Squamous Cell Carcinoma

The expression of PD-L1 by tumor cells is mainly associated with its immunosuppressive effect. In fact, PD-1/PD-L1 immune checkpoint inhibitors demonstrated remarkable effects in advanced cancer patients including HNSCC. In this context, irradiation is currently being investigated as a synergistic treatment modality to immunotherapy. However, the majority of HNSCC patients still show little improvement or even hyperprogression. Interestingly, there is increasing evidence for additional cell-intrinsic functions of PD-L1 in tumor cells. In previous studies, we showed that PD-L1 has a strong influence on proliferation, migration, invasion, and survival after irradiation. We demonstrated that cellular expression and localization of PD-L1 differed depending on sensitivity to irradiation. Here, we show that PD-L1 is also differentially expressed during cell cycle progression of HNSCC. Furthermore, cellular localization of PD-L1 also changes depending on a particular cell cycle phase. Moreover, distinct observations occurred depending on the general differentiation status. Overall, the function of PD-L1 cannot be generalized. Rather, it depends on the differentiation status and microenvironment. PD-L1 expression and localization are variable, depending on different factors. These findings may provide insight into why differential response to PD-1/PD-L1 antibody therapy can occur. Detailed understanding of cell-intrinsic PD-L1 functions will further allow antibody-based immunotherapy to be optimized