Law and Science through the Lens of Patent Law and Software Agents

In this investigation I look at patents and software agents as a way to study broader relation between law and science (the latter term broadly understood as inclusive of science and technology). The overall premise framing the entire discussion, my basic thesis, is that this relation, between law and science, cannot be understood without taking into account a number of intervening factors identifying which makes it necessary to approach the question from the standpoint of fields and disciplines other than law and science themselves.

Ex-post Liability Rules in Modern Patent Law

This doctoral thesis examines the use of liability rules to protect patent entitlements, focusing on a specific type of rule named ex-post since it is applied and designed ex-post by a court or an agency. The research starts from the premise that patents are defined by the legal and economic scholarship as exclusive rights but nevertheless, under certain circumstances there are economic as well as other compelling reasons to transform the exclusiveness of patent rights into a right to receive compensation.

Untersuchungen zur stereokontrollierten Synthese von 3-Mercaptolysinderivaten und Untersuchungen zur Synthese von Ansa-Seco-Steroiden

Untersuchungen zur stereokontrollierten Synthese von 3-Mercaptolysinderivaten: 3-Mercaptolysin und Peptide mit einer 3-Mercaptolysin-Einheit sind als Liganden für Nukleardiagnostika in der Kontrastmittelforschung von großem Interesse. Für das Screening Gewebe-selektiver Diagnostika sollten Mercaptolysinderivate, die sich für den Einbau in Peptide eignen, stereokontrolliert aufgebaut werden. Als Grundlage wurde im Rahmen dieser Arbeit eine sehr effiziente Synthese von Methoxymethyl-(4-oxo-butyl)-carbaminsäure-tert-butylester aus Pent-4-en-1-ol entwickelt. Nach Olefinierung des Aldehyds konnten Auxiliar-substituierte 6-Amino-hexensäure-derivate in guten Ausbeuten erhalten werden. Diese bildeten die Startmaterialien für auxiliargesteuerte Aziridinierungen mit anschließender Ringöffnung durch Schwefel-Nukleophile. Zudem wurden Azidierungen an Auxiliar-bewehrten Substraten, Michael-Additionen von Schwefel-Nukleophilen an Dehydroaminosäuren und viele weitere Reaktionen untersucht. Es galt dabei auf patentrechtlich geschützte Reaktionen zu verzichten, weil die Produkte ggf. in großem Maßstab kommerziell genutzt werden sollen. Dabei konnten alle vier stereoisomeren 2-Acetylamino-6-(tert-butoxycarbonyl-methoxymethyl-amino)-3-(4-methoxy-benzylsulfanyl)-hexansäurementhylester in guter Ausbeute synthetisiert werden. Nach Herstellung größerer Mengen der entsprechenden am Schwefelatom ungeschützten N-Fmoc-Aminosäurederivate sollen alle vier Stereoisomere in Peptide eingebaut und auf ihre Eignung als Liganden in Nukleardiagnostika untersucht werden. Untersuchungen zur Synthese neuartiger Ansa-Steroide: Über die Synthese von Ansa-Seco-Steroiden mittels eine Kaskade von intermolekularer Diels-Alder-Reaktion und anschließender Retro-Diels-Alder-Reaktion an 5,6,7,8-Tetradehydrosteroiden wurde erstmals 1986 von E. Winterfeldt et al. berichtet. Die damit eröffnete Möglichkeit eines völlig neuen Zugangs zu pharmakologisch interessanten Makrolid-Substraten konnte aber bislang nicht effektiv genutzt werden, weil insbesondere im Zusammenhang mit der Diels-Alder-Reaktion erhebliche präparative Probleme auftraten: Brauchbare Reaktivität nur bei sehr wenigen Dienophilen, Regioselektivitätsprobleme, etc. Hier galt es zu untersuchen, inwiefern sich diese Probleme durch die intramolekulare Reaktionsführung der Cycloaddition unterdrücken lassen können. Für die intramolekulare Diels-Alder-Reaktion zwischen der 5,7-Dien-Einheit des Steroids und einer an das Substrat gebundenen Dienophil-Einheit ist die Möglichkeit einer günstigen Anordnung der beiden Reaktanden-Gruppen entscheidend. Dafür wurden umfangreiche Untersuchungen zur alpha-konfigurierten Anbindung eines Dienophils in die 3-Position an 3-Hydroxy-5,6,7,8-Tetradehydrosteroiden durchgeführt: Mitsunobu-Reaktionen, Oxidations-Reduktions-Sequenzen, Oxidations-Ketalisierungs-Sequenzen, etc. Hierbei wurden zahlreiche neue Steroide synthetisiert. Es gelang jedoch nicht, 5,6,7,8-Tetradehydrosteroide mit axial in Position 3 eingebundenen Dienophilen in präparativ nutzbaren Mengen zu synthetisieren. Bei der Untersuchung intermolekularer Diels-Alder-Reaktionen zwischen Ergosterol und Brommaleinsäureanhydrid wurde vorzugsweise die Bildung der Addukte des 7,8,14,15-Tetradehydroisomers des Ergosterols beobachtet.

Automated Segmentation and Non-Rigid Registration for the Quantitative Evaluation of Myocardial Perfusion from Magnetic Resonance Images

Myocardial perfusion quantification by means of Contrast-Enhanced Cardiac Magnetic Resonance images relies on time consuming frame-by-frame manual tracing of regions of interest. In this Thesis, a novel automated technique for myocardial segmentation and non-rigid registration as a basis for perfusion quantification is presented. The proposed technique is based on three steps: reference frame selection, myocardial segmentation and non-rigid registration. In the first step, the reference frame in which both endo- and epicardial segmentation will be performed is chosen. Endocardial segmentation is achieved by means of a statistical region-based level-set technique followed by a curvature-based regularization motion. Epicardial segmentation is achieved by means of an edge-based level-set technique followed again by a regularization motion. To take into account the changes in position, size and shape of myocardium throughout the sequence due to out of plane respiratory motion, a non-rigid registration algorithm is required. The proposed non-rigid registration scheme consists in a novel multiscale extension of the normalized cross-correlation algorithm in combination with level-set methods. The myocardium is then divided into standard segments. Contrast enhancement curves are computed measuring the mean pixel intensity of each segment over time, and perfusion indices are extracted from each curve. The overall approach has been tested on synthetic and real datasets. For validation purposes, the sequences have been manually traced by an experienced interpreter, and contrast enhancement curves as well as perfusion indices have been computed. Comparisons between automatically extracted and manually obtained contours and enhancement curves showed high inter-technique agreement. Comparisons of perfusion indices computed using both approaches against quantitative coronary angiography and visual interpretation demonstrated that the two technique have similar diagnostic accuracy. In conclusion, the proposed technique allows fast, automated and accurate measurement of intra-myocardial contrast dynamics, and may thus address the strong clinical need for quantitative evaluation of myocardial perfusion.

Patent Strategies and R&D in Complex Product Industries

The study aims at providing a framework conceptualizing patenting activities under the condition of intellectual property rights fragmentation. Such a framework has to deal with the interrelated problems of technological complexity in the modern patent landscape. In that respect, ex-post licensing agreements have been incorporated into the analysis. More precisely, by consolidating the right to use patents required for commercialization of a product, private market solutions, such as cross-licensing agreements and patent pools help firms to overcome problems triggered by the intellectual property rights fragmentation. Thereby, private bargaining between parties as such cannot be isolated from the legal framework. A result of this analysis is that policies ignoring market solutions and only focusing on static gains can mitigate the dynamic efficiency gains as induced by the patent system. The evidence found in this thesis supports the opinion that legal reforms that aim to decrease the degree of patent protection or to lift it all together can hamper the functioning of the current system.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

Comparative DNA sequence and methylation analyses of orthologous genes in humans and non-human primates: Genetic and epigenetic footprints of evolution

Welche genetische Unterschiede machen uns verschieden von unseren nächsten Verwandten, den Schimpansen, und andererseits so ähnlich zu den Schimpansen? Was wir untersuchen und auch verstehen wollen, ist die komplexe Beziehung zwischen den multiplen genetischen und epigenetischen Unterschieden, deren Interaktion mit diversen Umwelt- und Kulturfaktoren in den beobachteten phänotypischen Unterschieden resultieren. Um aufzuklären, ob chromosomale Rearrangements zur Divergenz zwischen Mensch und Schimpanse beigetragen haben und welche selektiven Kräfte ihre Evolution geprägt haben, habe ich die kodierenden Sequenzen von 2 Mb umfassenden, die perizentrischen Inversionsbruchpunkte flankierenden Regionen auf den Chromosomen 1, 4, 5, 9, 12, 17 und 18 untersucht. Als Kontrolle dienten dabei 4 Mb umfassende kollineare Regionen auf den rearrangierten Chromosomen, welche mindestens 10 Mb von den Bruchpunktregionen entfernt lagen. Dabei konnte ich in den Bruchpunkten flankierenden Regionen im Vergleich zu den Kontrollregionen keine höhere Proteinevolutionsrate feststellen. Meine Ergebnisse unterstützen nicht die chromosomale Speziationshypothese für Mensch und Schimpanse, da der Anteil der positiv selektierten Gene (5,1% in den Bruchpunkten flankierenden Regionen und 7% in den Kontrollregionen) in beiden Regionen ähnlich war. Durch den Vergleich der Anzahl der positiv und negativ selektierten Gene per Chromosom konnte ich feststellen, dass Chromosom 9 die meisten und Chromosom 5 die wenigsten positiv selektierten Gene in den Bruchpunkt flankierenden Regionen und Kontrollregionen enthalten. Die Anzahl der negativ selektierten Gene (68) war dabei viel höher als die Anzahl der positiv selektierten Gene (17). Eine bioinformatische Analyse von publizierten Microarray-Expressionsdaten (Affymetrix Chip U95 und U133v2) ergab 31 Gene, die zwischen Mensch und Schimpanse differentiell exprimiert sind. Durch Untersuchung des dN/dS-Verhältnisses dieser 31 Gene konnte ich 7 Gene als negativ selektiert und nur 1 Gen als positiv selektiert identifizieren. Dieser Befund steht im Einklang mit dem Konzept, dass Genexpressionslevel unter stabilisierender Selektion evolvieren. Die meisten positiv selektierten Gene spielen überdies eine Rolle bei der Fortpflanzung. Viele dieser Speziesunterschiede resultieren eher aus Änderungen in der Genregulation als aus strukturellen Änderungen der Genprodukte. Man nimmt an, dass die meisten Unterschiede in der Genregulation sich auf transkriptioneller Ebene manifestieren. Im Rahmen dieser Arbeit wurden die Unterschiede in der DNA-Methylierung zwischen Mensch und Schimpanse untersucht. Dazu wurden die Methylierungsmuster der Promotor-CpG-Inseln von 12 Genen im Cortex von Menschen und Schimpansen mittels klassischer Bisulfit-Sequenzierung und Bisulfit-Pyrosequenzierung analysiert. Die Kandidatengene wurden wegen ihrer differentiellen Expressionsmuster zwischen Mensch und Schimpanse sowie wegen Ihrer Assoziation mit menschlichen Krankheiten oder dem genomischen Imprinting ausgewählt. Mit Ausnahme einiger individueller Positionen zeigte die Mehrzahl der analysierten Gene keine hohe intra- oder interspezifische Variation der DNA-Methylierung zwischen den beiden Spezies. Nur bei einem Gen, CCRK, waren deutliche intraspezifische und interspezifische Unterschiede im Grad der DNA-Methylierung festzustellen. Die differentiell methylierten CpG-Positionen lagen innerhalb eines repetitiven Alu-Sg1-Elements. Die Untersuchung des CCRK-Gens liefert eine umfassende Analyse der intra- und interspezifischen Variabilität der DNA-Methylierung einer Alu-Insertion in eine regulatorische Region. Die beobachteten Speziesunterschiede deuten darauf hin, dass die Methylierungsmuster des CCRK-Gens wahrscheinlich in Adaption an spezifische Anforderungen zur Feinabstimmung der CCRK-Regulation unter positiver Selektion evolvieren. Der Promotor des CCRK-Gens ist anfällig für epigenetische Modifikationen durch DNA-Methylierung, welche zu komplexen Transkriptionsmustern führen können. Durch ihre genomische Mobilität, ihren hohen CpG-Anteil und ihren Einfluss auf die Genexpression sind Alu-Insertionen exzellente Kandidaten für die Förderung von Veränderungen während der Entwicklungsregulation von Primatengenen. Der Vergleich der intra- und interspezifischen Methylierung von spezifischen Alu-Insertionen in anderen Genen und Geweben stellt eine erfolgversprechende Strategie dar.

Sequence-controlled synthesis of soluble oligo(p-benzamide)s

This work focused on the synthesis of novel monomers for the design of a series of oligo(p-benzamide)s following two approaches: iterative solution synthesis and automated solid phase protocols. These approaches present a useful method to the sequence-controlled synthesis of side-chain and main-chain functionalized oligomers for the preparation of an immense variety of nanoscaffolds. The challenge in the synthesis of such materials was their modification, while maintaining the characteristic properties (physical-chemical properties, shape persistence and anisotropy). The strategy for the preparation of predictable superstructures was devote to the selective control of noncovalent interactions, monodispersity and monomer sequence. In addition to this, the structure-properties correlation of the prepared rod-like soluble materials was pointed. The first approach involved the solution-based aramide synthesis via introduction of 2,4-dimethoxybenzyl N-amide protective group via an iterative synthetic strategy The second approach focused on the implementation of the salicylic acid scaffold to introduce substituents on the aromatic backbone for the stabilization of the OPBA-rotamers. The prepared oligomers were analyzed regarding their solubility and aggregation properties by systematically changing the degree of rotational freedom of the amide bonds, side chain polarity, monomer sequence and degree of oligomerization. The syntheses were performed on a modified commercial peptide synthesizer using a combination of fluorenylmethoxycarbonyl (Fmoc) and aramide chemistry. The automated synthesis allowed the preparation of aramides with potential applications as nanoscaffolds in supramolecular chemistry, e.g. comb-like-

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.

Patent nonnuse: are patent pools as possible solution?

Studies have depicted that the rate of unused patents comprises a high portion of patents in North America, Europe and Japan. Particularly, studies have identified a considerable share of strategic patents which are left unused due to pure strategic reasons. While such patents might generate strategic rents to their owner, they may have harmful consequences for the society if by blocking alternative solutions that other inventions provide they hamper the possibility of better solutions. Accordingly, the importance of the issue of nonuse is highlighted within the literature on strategic patenting, IPR policy and innovation economics. Moreover, the current literature has emphasized on the role of patent pools in dealing with potential issues such as excessive transaction cost caused by patent thickets and blocking patents. In fact, patent pools have emerged as policy tools facilitating technology commercialization and alleviating patent litigation among rivals holding overlapping IPRs. In this dissertation I provide a critical literature review on strategic patenting, identify present gaps and discuss some future research paths. Moreover, I investigate the drivers of strategic non-use of patents with particular focus on unused strategic play patents. Finally, I examine if participation intensity in patent pools by pool members explains their willingness to use their non-pooled patents. I also investigate which characteristics of the patent pools are associated to the willingness to use non-pooled patents through pool participation. I show that technological uncertainty and technological complexity are two technology environment factors that drive unused play patents. I also show that pool members participating more intensively in patent pools are more likely to be willing to use their non-pooled patents through pool participation. I further depict that pool licensors are more likely to be willing to use their non-pooled patents by participating in pools with higher level of technological complementarity to their own technology.

Molekulare Analyse des Kolumnar-Gens beim Apfel (Malus x domestica)

Das Kolumnarwachstum beim Apfel (Malus x domestica) geht auf eine in den frühen 1960er Jahren entdeckte Zufallsmutation zurück. Die daraus resultierende Sprossmutante ist von großem wirtschaftlichem Interesse, da diese sehr kompakte Wuchsform unter anderem zu einer enormen Ertragssteigerung durch eine hohe Pflanzdichte der Bäume führt. Das Ziel der Arbeit ist die Entschlüsselung der molekularen Ursache dieser Mutation, die bisher weitgehend ungeklärt ist. Die Analyse wurde durch die Erstellung einer Referenzsequenz der Co-Zielregion einer kolumnaren Apfelsorte sowie durch die Konstruktion eng gekoppelter molekularer Marker realisiert. Durch die Konstruktion von genomischen Apfel-BAC-Bibliotheken mit mehrfacher Genomabdeckung und die Erstellung geeigneter Sonden wurde die Co-Region kloniert und deren Sequenz bestimmt. In Kombination zu dieser klassischen positionellen Klonierungsstrategie wurden genomische Illumina „mate pair“-Bibliotheken erstellt, sequenziert und bioinformatisch analysiert, um die genomische Region vollständig zu annotieren. Somit wurde eine vollständige genomische Referenz der Co-Region einer kolumnaren Apfelsorte erstellt, die die Grundlage für weitere Analysen bildet. Auf Basis dieser Referenz konnte die Co-Mutation in Form der Integration des LTR-Retrotransposons Gypsy-44 im kolumnaren Chromosom an Position 18,79 Mbp auf Chromosom 10 lokalisiert werden. Darüber hinaus konnten Transposon-basierende molekulare Marker erstellt werden, die eine verlässliche Genotypisierung von Apfelbäumen in Bezug auf das Kolumnarwachstum ermöglichen und dies unabhängig von der verwendeten Apfelsorte. Der genaue Wirkmechanismus von Gypsy-44, der zur Ausprägung dieses extremen Phänotyps führt, ist bislang unklar. Zusammenfassend lässt sich sagen, dass die molekulare Ursache für das kolumnare Wachstum aufgeklärt werden konnte und zudem die ersten molekularen Marker erstellt wurden, die eine sortenunabhängige Differenzierung zwischen kolumnaren und nicht kolumnaren Apfelbäumen ermöglichen.

Predicting CMS datasets popularity with machine learning

In CMS è stato lanciato un progetto di Data Analytics e, all’interno di esso, un’attività specifica pilota che mira a sfruttare tecniche di Machine Learning per predire la popolarità dei dataset di CMS. Si tratta di un’osservabile molto delicata, la cui eventuale predizione premetterebbe a CMS di costruire modelli di data placement più intelligenti, ampie ottimizzazioni nell’uso dello storage a tutti i livelli Tiers, e formerebbe la base per l’introduzione di un solito sistema di data management dinamico e adattivo. Questa tesi descrive il lavoro fatto sfruttando un nuovo prototipo pilota chiamato DCAFPilot, interamente scritto in python, per affrontare questa sfida.

Patent arterial duct, bottle-meal, and fatal myocardial ischaemia

A patent arterial duct in pre-term neonates is frequent. Systemic complications consecutive to left-to-right shunting are well known but fatal myocardial ischaemia has not been described till now. The presented premature baby died from catecholamine refractory cardiogenic shock. Autoptic examination revealed acute ischaemic changes predominantly in the inner third of myocardium, speaking of coronary hypoperfusion due to a steal phenomenon secondary to the patent arterial duct.