Towards an Intellectual Property Rights Strategy for Innovation in Europe

On October 13, 2009 the Science and Technology Options Assessment Panel (STOA) together with Knowledge4Innovation/The Lisbon Forum, supported by Technopolis Consulting Group and TNO, organised a half-day workshop entitled ‘Towards an Intellectual Property Rights Strategy for Innovation in Europe’. This workshop was part of the 1st European Innovation Summit at the European Parliament which took place on 13 October and 14 October 2009. It addressed the topics of the evolution and current issues concerning the European Patent System as well as International Protection and Enforcement of IPR (with special consideration of issues pertaining to IP enforcement in the Digital Environment). Conclusions drawn point to the benefits of a comprehensive European IPR strategy, covering a broad range of IP instruments and topics

The Bet v 1 fold: an ancient, versatile scaffold for binding of large, hydrophobic ligands

<p>Abstract</p> <p>Background</p> <p>The major birch pollen allergen, Bet v 1, is a member of the ubiquitous PR-10 family of plant pathogenesis-related proteins. In recent years, a number of diverse plant proteins with low sequence similarity to Bet v 1 was identified. In addition, determination of the Bet v 1 structure revealed the existence of a large superfamily of structurally related proteins. In this study, we aimed to identify and classify all Bet v 1-related structures from the Protein Data Bank and all Bet v 1-related sequences from the Uniprot database.</p> <p>Results</p> <p>Structural comparisons of representative members of already known protein families structurally related to Bet v 1 with all entries of the Protein Data Bank yielded 47 structures with non-identical sequences. They were classified into eleven families, five of which were newly identified and not included in the Structural Classification of Proteins database release 1.71. The taxonomic distribution of these families extracted from the Pfam protein family database showed that members of the polyketide cyclase family and the activator of Hsp90 ATPase homologue 1 family were distributed among all three superkingdoms, while members of some bacterial families were confined to a small number of species. Comparison of ligand binding activities of Bet v 1-like superfamily members revealed that their functions were related to binding and metabolism of large, hydrophobic compounds such as lipids, hormones, and antibiotics. Phylogenetic relationships within the Bet v 1 family, defined as the group of proteins with significant sequence similarity to Bet v 1, were determined by aligning 264 Bet v 1-related sequences. A distance-based phylogenetic tree yielded a classification into 11 subfamilies, nine exclusively containing plant sequences and two subfamilies of bacterial proteins. Plant sequences included the pathogenesis-related proteins 10, the major latex proteins/ripening-related proteins subfamily, and polyketide cyclase-like sequences.</p> <p>Conclusion</p> <p>The ubiquitous distribution of Bet v 1-related proteins among all superkingdoms suggests that a Bet v 1-like protein was already present in the last universal common ancestor. During evolution, this protein diversified into numerous families with low sequence similarity but with a common fold that succeeded as a versatile scaffold for binding of bulky ligands.</p

The use of trade secrets to protect data shared between firms in agricultural and food sectors

Both public policy and business management are increasingly interested in how to manage trade secrets. One of the driving forces is the growing significance of data as an asset, as ‘oil of the 21st century'. Trade secrets are often seen as the major Intellectual Property (IP) tool for protecting data. There is also the understanding that the need to share data is increasing to allow for new types of innovation. This paper seeks to understand how data sharing practices and the use of trade secrets are evolving in the agricultural industries. Using explorative empirical data from four in-depth case studies, the paper develops a framework for data sharing practices, value sharing, and trade secrets use. We find that current data sharing practices pool around two scenarios, where data is not shared or shared only with limited partners (hence closed) and there are differences whether value created from the data is shared. We conclude that a nuanced view on the use of trade secrets in data sharing is mandated for both IP/data managers and scholars analysing the topic

Herzratenvariabilität bei PatientInnen mit Depression oder der Doppeldiagnose Depression - somatoforme Störungen im Therapieverlauf

Theoretischer Hintergrund: Die Herzratenvariabilität (HRV) gibt Aufschluss über die sympathische und parasympathische Aktivität des autonomen Nervensystems. Eine niedrige HRV stellt ein Mortalitäts- und Morbiditätsrisiko dar und wird auch mit Depression und verschiedenen funktionelle Syndromen in Verbindung gebracht, wobei die Ergebnisse zur HRV bei Depression heterogen sind und eine mögliche komorbide somatoforme Störung oft unberücksichtigt bleibt. Die HRV gilt als Parameter für Gesundheit und Anpassungsfähigkeit und dürfte sich zur Therapieevaluation eignen. Fragestellung: Ziel war es, die HRV bei PatientInnen mit Depression sowie der Doppeldiagnose Depression und somatoforme Störung im Therapieverlauf zu untersuchen und im speziellen auf die Veränderungen durch einen mehrwöchigen stationären Therapieaufenthalt einzugehen. Zudem wurde auch auf die prognostische Bedeutung der HRV für den Therapieerfolg eingegangen werden. Methode: Es nahmen 32 PatientInnen mit depressiver Erkrankung (15 mit Depression, 17 mit Depression und somatoforme Störung) an der Studie teil. Für diese quasiexperimentelle Studie fanden zu Beginn und zu Ende eines etwa neunwöchigen stationären Aufenthalts Messungen statt. Für die 24-Stunden-HRV-Messung wurde der Medilog® AR12plus Digitaler Holter Rekorder eingesetzt. Es wurden neben den Zeit- und Frequenzbereichsparametern SDNN, pNN50, HF-HRV, LF-HRV und VLF-HRV auch die SD1 und SD2 aus dem Poincarégraph sowie die nichtlinearen Parameter DFA α1 und α2 und die Sample Entropie ausgewertet. Als psychologisch-diagnostische Verfahren wurden die Symptomcheckliste-90-Revidiert (SCL-90-R) sowie der Fragebogen zum Gesundheitszustand SF-36 verwendet. Ergebnisse: Die HRV von PatientInnen mit und ohne somatoformer Störung unterschied sich weder in Variabilität noch Komplexität. PatientInnen die Antidepressiva einnahmen hatten eine deutlich reduzierte HRV, wobei diese Unterschiede nicht auf unterschiedliche Schweregrade der Depression zurückgeführt werden konnten. In der Gesamtstichprobe kam es im Therapieverlauf zu einer signifikanten Steigerung der nächtlichen Gesamt- und langfristigen Variabilität wie auch der VLF-HRV. PatientInnen die Antidepressiva einnahmen, zeigten zudem in der Gesamtmessung eine Steigerung des Vagotonus. PatientInnen mit höheren HRV-Werten zu Therapiebeginn konnten besser vom stationären Aufenthalt profitieren. Die SDNN eignete sich zudem zur Vorhersage einer Verbesserung der grundsätzlichen psychischen Belastung und der Depressivität sowie der Werte für Somatisierung, körperlicher Gesundheit und der Beeinträchtigung durch körperliche Schmerzen.Theoretical background: Heart rate variability (HRV) provides information about the sympathetic and parasympathetic activity of the autonomic nervous system. A low HRV poses a mortality and morbidity risk. It is associated with depression and various functional disorders. However, results for HRV in depression are heterogeneous and a possible comorbid somatoform disorder is often disregarded. Moreover, HRV is considered to be an indicator for health and flexibility and should be suitable for the evaluation of therapy. Objectives: The aim was to analyze the HRV of inpatients with depression or with the double diagnosis of depression and somatoform disorder in the course of treatment. A focus lay on examining changes due to a nine week treatment phase. Additionally, the prognostic value of HRV for therapy outcome was evaluated. Method: 32 patients with depressive disorders (15 with depression, 17 with depression and somatoform disorder) were included in the study. In this quasi-experimental study, assessment took place at the beginning and the end of a nine week clinical treatment. To measure the HRV over the course of 24 hours a Medilog® AR12plus digital holter recorder was used. Parameters analyzed were the time and frequency domain measures SDNN, pNN50, HF-HRV, LF-HRV and VLF-HRV, the SD1 und SD2 from the Poincaré plot as well as the nonlinear measures DFA α1 and α2 and the Sample Entropy. The Symptom Checklist-90-Revised (SCL-90-R) and the SF-36 Health Survey were used for psychological assessment. Results: The HRV of patients with and without somatoform disorder differed neither in variability nor in complexity. Patients taking antidepressants had a significantly reduced HRV, which was not attributable to severity of depression. Overall, we observed a significant increase in the nocturnal total variability and the long-term variability as well as the VLF-HRV throughout the course of therapy. Patients taking antidepressants showed an increase in vagal tone. Patients with higher HRV at the beginning of the treatment benefitted more from treatment. SDNN was found to predict improvements with regard to overall psychological distress and the depression as well as somatization, physical health, and bodily pain

Hagenbeck's anthropologisch-zoologische Kalmücken Ausstellung

Die Völkerschau der KalmückInnen von 1883 wird untersucht und analysiert. Die Anwerbung, Anreise, der Aufenthalt und die Durchführung der Völkerschau sowie deren Rezeption bei dem Publikum, den Medien und WissenschafterInnen werden thematisiert

Effects of Supplementary Protection Mechanisms for Pharmaceutical Products

Rapport over beschermingsmechanismen op het gebied van intellectueel eigendom met betrekking tot medicijnen

Study on the legal protection of trade secrets in the context of the data economy : final report

This study analyses a) to what extent the EU legal framework on trade secret protection applies to data which is shared across firms and organisations and b) the application of trade secrets by European firms in practice. It is set against the backdrop of the growing significance of the data economy and data sharing. The study finds that while the significance of data sharing has been and will be increasing, the protection and appropriation of shared data with trade secrets is lagging. Only a few firms are truly familiar with the application of trade secrets in the context of shared data. On the one hand, this might partly be attributed to the rather young age of the EU Trade Secrets Directive (TSD) and still developing IP management practices of many firms that take due account of trade secrets. On the other hand, and because of the lack of developed jurisprudence, many firms are uncertain regarding the exact meaning of some of the terminology which defines trade secrets as well as regarding actual enforceability. Part of this uncertainty can be alleviated through legal reasoning, but some parts may need further clarification or jurisprudence to develop. In practice, trade secrets are used mostly as a second layer of protection after contracts (which are clearly the preferred mode of protection) as well as a tool against misappropriation by third parties with whom no contractual relations exist. The study develops recommendations in the areas of a) operationally improving firm performance when using trade secrets for shared data; b) reducing possible ambiguity and improving clearness when interpreting the TSD; and c) improving and monitoring the legal framework surrounding the use of trade secrets for protecting shared confidential and commercially valuable data

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models.

In modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potentially arising problems in product quality, the second phase concerns proper reactions upon the recognition of such problems. In this paper, we address a holistic approach for handling both issues consecutively within a predictive maintenance framework at an on-line production system. Thereby, we address multi-stage functionality based on (i) data-driven forecast models for (measure-able) product quality criteria (QCs) at a latter stage, which are established and executed through process values (and their time series trends) recorded at an early stage of production (describing its progress), and (ii) process optimization cycles whose outputs are suggestions for proper reactions at an earlier stage in the case of forecasted downtrends or exceeds of allowed boundaries in product quality. The data-driven forecast models are established through a high-dimensional batch time-series modeling problem. In this, we employ a non-linear version of PLSR (partial least squares regression) by coupling PLS with generalized Takagi–Sugeno fuzzy systems (termed as PLS-fuzzy). The models are able to self-adapt over time based on recursive parameters adaptation and rule evolution functionalities. Two concepts for increased flexibility during model updates are proposed, (i) a dynamic outweighing strategy of older samples with an adaptive update of the forgetting factor (steering forgetting intensity) and (ii) an incremental update of the latent variable space spanned by the directions (loading vectors) achieved through PLS; the whole model update approach is termed as SAFM-IF (self-adaptive forecast models with increased flexibility). Process optimization is achieved through multi-objective optimization using evolutionary techniques, where the (trained and updated) forecast models serve as surrogate models to guide the optimization process to Pareto fronts (containing solution candidates) with high quality. A new influence analysis between process values and QCs is suggested based on the PLS-fuzzy forecast models in order to reduce the dimensionality of the optimization space and thus to guarantee high(er) quality of solutions within a reasonable amount of time (→ better usage in on-line mode). The methodologies have been comprehensively evaluated on real on-line process data from a (micro-fluidic) chip production system, where the early stage comprises the injection molding process and the latter stage the bonding process. The results show remarkable performance in terms of low prediction errors of the PLS-fuzzy forecast models (showing mostly lower errors than achieved by other model architectures) as well as in terms of Pareto fronts with individuals (solutions) whose fitness was close to the optimal values of three most important target QCs (being used for supervision): flatness, void events and RMSEs of the chips. Suggestions could thus be provided to experts/operators how to best change process values and associated machining parameters at the injection molding process in order to achieve significantly higher product quality for the final chips at the end of the bonding process

On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks.

Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detection in multi-sensor networks rely on models which are extracted from multi-sensor measurements and which characterize the anomaly-free reference situation. Therefore, significant deviations to these models indicate potential anomalies. In this paper, we propose a new approach which is based on causal relation networks (CRNs) that represent the inner causes and effects between sensor channels (or sensor nodes) in form of partial sub-relations, and evaluate its functionality and performance on two distinct production phases within a micro-fluidic chip manufacturing scenario. The partial relations are modeled by non-linear (fuzzy) regression models for characterizing the (local) degree of influences of the single causes on the effects. An advanced analysis of the multi-variate residual signals, obtained from the partial relations in the CRNs, is conducted. It employs independent component analysis (ICA) to characterize hidden structures in the fused residuals through independent components (latent variables) as obtained through the demixing matrix. A significant change in the energy content of latent variables, detected through automated control limits, indicates an anomaly. Suppression of possible noise content in residuals—to decrease the likelihood of false alarms—is achieved by performing the residual analysis solely on the dominant parts of the demixing matrix. Our approach could detect anomalies in the process which caused bad quality chips (with the occurrence of malfunctions) with negligible delay based on the process data recorded by multiple sensors in two production phases: injection molding and bonding, which are independently carried out with completely different process parameter settings and on different machines (hence, can be seen as two distinct use cases). Our approach furthermore i.) produced lower false alarm rates than several related and well-known state-of-the-art methods for (unsupervised) anomaly detection, and ii.) also caused much lower parametrization efforts (in fact, none at all). Both aspects are essential for the useability of an anomaly detection approach