Gains and pains from the open innovation framework

While firms increasingly adopt open innovation, little is known about whether firms gain or lose from this innovation approach. Motivated by this research gap in the literature, the thesis explores the antecedents and performance implications of open innovation strategies, particularly collaboration and contractual forms of relationships for innovation (i.e. innovation cooperation and R&D outsourcing/external R&D). The thesis is empirical and relates the various results to the data used. The first one with German Community Innovation Survey (CIS), the second one with Danish CIS and the third one with the patent enhanced German CIS. The empirical analyses suggest that a value-enhancing objective rather than a cost-minimization purpose is the main factor that stimulates companies to engage in open innovation strategies. The research also reveals that firms engage in various innovation strategies simultaneously (i.e. international external R&D, innovation cooperation partnerships and internal R&D), but they fail to combine these instruments successfully for product innovation, implying that the single innovation strategy is performing better than combining different knowledge sourcing strategies in open innovation. Furthermore, the thesis provides evidence that sourcing R&D inputs from a domestic R&D provider can be a risky strategy when a firm aims to generate breakthrough product innovations. Instead, the firm should seek to acquire knowledge inputs from international marketplaces. The research also indicates that those firms outsourcing R&D activities are more likely to generate inventions than their counterparts that do not invest in this R&D strategy. However, this positive performance implication of R&D outsourcing does not appear to hold for invention quality

Exploring the animal GPS system: a machine learning approach to study the hippocampal function

2014. aasta Nobeli preemia füsioloogias said Dr. John M. O’Keefe, Dr. May-Britt Moser ja Dr. Edvard I teatud kindlate rakkude avastamise eest ajus, mis vastutavad ruumi- ja suunataju eest. Need avastused võimaldavad arvata, et aju loob sisemise kaardi ümbritsevast keskkonnast. See aitab meil ära tunda tuttavaid kohti ja ruumis hästi orienteeruda. Antud magistritöös kasutasime me roti “GPS” süsteemi tundma õppimiseks arvutuslikku lähenemist. Konkreetsemalt võrdlesime, kui hästi suudavad erinevad masinõppe algoritmid ette ennustada roti asukohta, saades sisendiks ainult tema hipokampuses toimuva neuronaalse aktiivsuse. Võrreldud meetodite seas olid juhumets (random forest), tugivektorklassifitseerijad (support vector machine, SVM), lähima naabri meetod (nearest neighbor) ja mõningad hajusa lineaarse regressiooni algoritmid. Neuronitest mõõdetud elektrofüsioloogilised andmed olid pärit Buxsaki laborist New Yorgis. Keskendusime roti hipokampuse neuraalsele aktiivsusele - aju osale, kus on senistes uurimustöödes enamik koharakke tuvastatud. Esimese sammuna jagasime ala, kus rott eksperimendi ajal viibis, neljaks väiksemaks tsooniks. Seejärel üritasime ennustada, missuguses alas katsealune loom mingil suvalisel ajahetkel asus. Leidsime, et juhumets andis parima ennustustäpsuse, milleks oli 57.8% ja mis on oluliselt suurem juhusliku valiku tõenäosusest. Sellegipoolest oli mõnedes katseala regioonides tugivektorklassfitseerija mõnikord parem kui juhumets. Järgmise sammuna tegime asukoha identifitseerimise veelgi raskemaks ja jagasime eksperimentaalala 16 väiksemaks tsooniks. Juhumets ja SVM saavutasid tugevalt statisiliselt olulised tulemused, vastavalt 38% ja 37% (juhusliku ennustuse täpsus oleks olnud umbes 11%). Mõlema probleemülesande puhul kasutasime me ka lähima naabri algoritmi, aga selle täpsus oli võrreldes eelmainitud meetoditega märgatavalt väiksem. Kuna roti asukoht on pidev muutuja, siis me proovisime käsitleda seda ka pideva ennustuse probleemina. Suurem osa regressiooni algoritme, mida selles töös analüüsitakse (kantregressioon (ridge regression), lassoregressioon (lasso regression), elastne võrk (elastic net)), andsid juhuslikule ennustustäpsusele lähedasi tulemusi. Ainult juhumets andis pideva ennustuse probleemi puhul teistest meetoditest oluliselt parema täpsuse. Seejärel analüüsisime me andmeid, mis olid salvestatud eksperimendist, kus rotid olid treenitud valima vasakut või paremat suunda number 8 kujulises labürindis, olles samal ajal ise jooksurattal. Nende mõõtmistulemuste puhul teostasime me esimese sammuna andmetele mõõdete vähendamise (dimensionality reduction), et visualiseerida muutusi andmetes otsuse langetamise hetkel. Muuhulgas identifitseerisime ja tõime joonistel välja ka episoodirakud - neuronid, mis on rohkem aktiivsed kindlal ajal antud ülesande jooksul. Episoodirakud võivad kaasa aidata aja tajumisel ja episoodilise mälu loomisel. Samuti visualiseerisime neuronaalseid trajektoore otsuse langetamise ajal, et ette aimata, millise otsuse loom vastu võtab. Kokkuvõtteks andis roti asukoha ennustamisel algoritmidest täpseimaid tulemusi juhumets. See võib muuhulgas näidata seda, et informatsioon roti asukoha kohta sisaldub mitte-lineaarses neuraalses aktiivsuses, mida lineaarregressiooni meetodid ei olnud võimelised tuvastama. Edasises uurimistöös plaanime me dekodeerida roti asukohta, kasutades meetodeid, mis on sarnasemad aju enda mehhanismidele. Neurovõrgud (neural networks) on laialt levinud masinõppe meetod, mis sarnaselt juhumetsadega suudab ära tunda mitte-lineaarseid mustreid. Selles töös loodud andmetöötluskonveiereid (data processing pipeline), mis tegelevad üsnagi keerulise andmete eeltöötluse, tunnuste eraldamise ja andmestiku visualiseerimisega, panevad tulevikuks tugeva aluse hipokampuse dünaamika uurimisele TÜ arvutusliku neuroteaduse töögrupis.The 2014 Nobel prize in Physiology was awarded to Dr. John M. O’Keefe, Dr. May-Britt Moser and Dr. Edvard I for discovering particular cells in the brain that provide the sense of place and navigation. These discoveries suggest that the brain creates internal map-like representation of the environment which helps us recognize familiar places and navigate well. In this thesis, we used a computational approach to study the animal "GPS" system. In particular, we set to compare how well different machine learning algorithms are able to predict a rat's position just based on its hippocampal neural activity. Methods compared included Random Forest, Support Vector Machines, k-Nearest Neighbors, and several sparse linear regression algorithms. Data was obtained multi-neuron electrophysiological data recorded from the Buzsaki lab in New York, and we focus on the activity of rat hippocampus, the brain region where most the place cells have been identified. In a first step, we divided the experimental arena into 4 blocks and tried to classify in which one of those blocks the rat was at a given time. In this case, we found that Random Forest gave the best accuracy which was 57.8%, well beyond the chance level. However, in some particular regions of the arena, Support Vector Machine was sometimes better than Random Forest. For the next step, we made the classification problem even harder by dividing the arena into 16 blocks. Random Forest and SVM produced highly significant results with 38% and 37% accuracy respectively (random classifier accuracy would be approximately ~11%). We also used K-Nearest Neighbors for both classification problems but its accuracy was less in both cases than the above mentioned algorithms. Since the rat position is a continuous variable we also considered the continuous prediction problem. Most regression algorithms we analyzed (Ridge Regression, LASSO, Elastic Net) provided results near chance level while Random Forest outperformed the algorithms and gave the best results in this case. Furthermore, we analysed data recorded from an experiment where rats were trained to choose left or right direction in a 8-shaped maze while they were running in a wheel. In this case we perform a dimensionality reduction of the neuronal data to visualize its dynamics during the decision time. We also identified and provided plots of episodic cells (neurons who are more active at particular times in the task) which might contribute to the sense of time and create episodic memory. Also, we visualized neuronal trajectories while animal makes decisions in order to predict its future decision. In conclusion, from the algorithms we analysed Random Forest gave the best accuracy while predicting a rat's location. This might also indicate that the information about rat location is contained in non-linear patterns of neuronal activity, which linear regression methods were unable to extract. In future research we plan to decode a rat position using a method more similar to the brain own mechanisms such as neural networks, which as Random Forest can detect non-linear patterns. More generally, the pipelines developed during this thesis to handle the complex pre-processing, feature extraction, and visualization of the dataset will set the basis for future studies on hippocampal dynamics by the group of computational neuroscience in the University of Tartu

Machine learning and data-parallel processing for viral metagenomics

More than 2 million cancer cases around the world each year are caused by viruses. In addition, there are epidemiological indications that other cancer-associated viruses may also exist. However, the identification of highly divergent and yet unknown viruses in human biospecimens is one of the biggest challenges in bio- informatics. Modern-day Next Generation Sequencing (NGS) technologies can be used to directly sequence biospecimens from clinical cohorts with unprecedented speed and depth. These technologies are able to generate billions of bases with rapidly decreasing cost but current bioinformatics tools are inefficient to effectively process these massive datasets. Thus, the objective of this thesis was to facilitate both the detection of highly divergent viruses among generated sequences as well as large-scale analysis of human metagenomic datasets. To re-analyze human sample-derived sequences that were classified as being of “unknown” origin by conventional alignment-based methods, we used a meth- odology based on profile Hidden Markov Models (HMM) which can capture evolutionary changes by using multiple sequence alignments. We thus identified 510 sequences that were classified as distantly related to viruses. Many of these sequences were homologs to large viruses such as Herpesviridae and Mimiviridae but some of them were also related to small circular viruses such as Circoviridae. We found that bioinformatics analysis using viral profile HMM is capable of extending the classification of previously unknown sequences and consequently the detection of viruses in biospecimens from humans. Different organisms use synonymous codons differently to encode the same amino acids. To investigate whether codon usage bias could predict the presence of virus in metagenomic sequencing data originating from human samples, we trained Random Forest and Artificial Neural Networks based on Relative Synonymous Codon Usage (RSCU) frequency. Our analysis showed that machine learning tech- niques based on RSCU could identify putative viral sequences with area under the ROC curve of 0.79 and provide important information for taxonomic classification. For identification of viral genomes among raw metagenomic sequences, we devel- oped the tool ViraMiner, a deep learning-based method which uses Convolutional Neural Networks with two convolutional branches. Using 300 base-pair length sequences, ViraMiner achieved 0.923 area under the ROC curve which is con- siderably improved performance in comparison with previous machine learning methods for virus sequence classification. The proposed architecture, to the best of our knowledge, is the first deep learning tool which can detect viral genomes on raw metagenomic sequences originating from a variety of human samples. To enable large-scale analysis of massive metagenomic sequencing data we used Apache Hadoop and Apache Spark to develop ViraPipe, a scalable parallel bio- informatics pipeline for viral metagenomics. Comparing ViraPipe (executed on 23 nodes) with the sequential pipeline (executed on a single node) was 11 times faster in the metagenome analysis. The new distributed workflow contains several standard bioinformatics tools and can scale to terabytes of data by accessing more computer power from the nodes. To analyze terabytes of RNA-seq data originating from head and neck squamous cell carcinoma samples, we used our parallel bioinformatics pipeline ViraPipe and the most recent version of the HPV sequence database. We detected transcription of HPV viral oncogenes in 92/500 cancers. HPV 16 was the most important HPV type, followed by HPV 33 as the second most common infection. If these cancers are indeed caused by HPV, we estimated that vaccination might prevent about 36 000 head and neck cancer cases in the United States every year. In conclusion, the work in this thesis improves the prospects for biomedical researchers to classify the sequence contents of ultra-deep datasets, conduct large- scale analysis of metagenome studies, and detect presence of viral genomes in human biospecimens. Hopefully, this work will contribute to our understanding of biodiversity of viruses in humans which in turn can help exploring infectious causes of human disease

Studies on tumor virus epidemiology

The causal relationship between several virus infections and human cancers are well established. However, it is also possible that additional cancers may be caused by known or yet unknown viruses. The present thesis has sought to both further elucidate known relationships between virus and cancer as well as to provide a basis for further exploration in the area of infections and cancer. Infections during pregnancy have been suspected to be involved in the etiology of childhood leukemias. However, no specific infectious agent is yet linked to the etiology of these diseases. As a basis for further studies in this area, we applied high-throughput next generation sequencing (NGS) technology to describe the viruses most readily detectable in serum samples of mothers to leukemic children. The most common viruses found were TT viruses, including several previously not described TT viruses. Merkel cell polyomavirus (MCV) is found in Merkel cell carcinoma (MCC), a rare and aggressive neuroendocrine tumor of the skin. To explore whether MCV infection might be associated with additional cancers, we investigated whether MCC patients are at excess risk of other cancers, using population-based Nordic cancer registries. Bidirectional evaluation of excess risk of other diseases among MCC patients revealed that they are at increased risk of other skin cancers as a second cancer, compared to the general Nordic population. Shared causative factors, such as exposure to ultraviolet light and/or MCV infection are among the possible explanations. Also, impact of increased surveillance of the skin should be noted as an explanation of the excess risk. Cutaneous human papillomaviruses (HPV) are suspected to be involved in the etiology of non-melanoma skin cancer (NMSC). To evaluate whether there are any consistent association between cutaneous HPV infections and skin cancer, we conducted a systematic review and meta-analysis of studies that investigated HPV prevalences among cases of skin lesions and their healthy controls. We found that HPV species Beta-1, Beta-2, Beta-3 and Gamma-1 were more frequently detected in squamous cell carcinoma (SCC) compared to healthy controls. To provide clues about possible carcinogenicity of 47 mucosal HPV types, out of which 12 are established as causes of cervical cancer, we also investigated the prevalence of 47 mucosal HPV types across the entire range of cervical diagnoses from normal to cervical cancer. To investigate diversity of HPVs in skin lesions with increased sensitivity, different sample types from different skin lesion were subjected to high-throughput NGS after PCR amplification. Conventional molecular detection methods such as PCR are biased towards the primers used. Thus they might miss viruses that are divergent from the primer sequences. We also investigated whether NGS technology can be used to assess presence of virus DNA in an unbiased manner, both in skin lesions as well as in condylomas that were classified as “HPV negative” by conventional PCR methods. Unbiased sequencing identified two putatively new HPV types that were missed by NGS after PCR amplification. The advantage of unbiased sequencing over conventional molecular detection methods was further demonstrated in the study of “HPV negative” condylomas. We found several known as well as several putatively novel HPV types in condylomas that were previously found to be HPV negative by PCR. In conclusion, we have used registry linkage studies, systematic reviews and metaanalyses and modern NGS technology applied to biobanked specimens to extend our knowledge of the epidemiology of cancer-associated viruses and to provide a basis for further exploration in this area

“Frenemies” of innovation: understanding the role of coopetition in service innovation in emerging markets

open access articleCoopetition is considered an important strategy for innovation. However, the literature provides limited evidence on how coopetition relates to innovation in service sector, particularly in emerging markets. Moreover, little is known about the effects of the formal and informal aspects of coopetition on innovation and how absorptive capacity of firm may influence this relationship. Against this background, using the official national innovation surveys of Nigeria (2008 and 2011), this study contributes to the ongoing debate by empirically examining the innovation endeavors of 421 Nigerian SMEs. The study employs logistic regression methods to model and explore the relationships between coopetition and innovation in the sample. The results show that that formal coopetition hinders innovation while informal coopetition supports it and absorptive capacity moderates these relationships. The study provides important insights about the concept of coopetition in emerging markets, especially vis-à-vis their institutional idiosyncrasies. Finally, the study highlights its implications and suggests some avenues for future research

Data-based Startup Profile Analysis in the European Smart Specialization Strategy: A Text Mining Approach

AbstractThe aim of the paper is to develop novel scientific metrics approach to the European Smart Specialization Strategy. The European Union (EU) has introduced Smart Specialization Strategy (S3) to increase the innovation and competitive potential of its member states by identifying promising economic areas for investment and specialization. While the evaluation of Smart Specialization Strategy requires measurable criteria for the comparison of rate and level of development of countries and regions, policy makers lack efficient and viable tools for mapping promising sectors for smart specialization. To cope with this issue, we used a text mining approach to analyze the business description of startups from Nordic and Baltic countries in order to identify sectors in which entrepreneurs from these regions see new business opportunities. In particular, a topic modeling, Latent Dirichlet Allocation approach is employed to classify business descriptions and to identify sectors, in which start-up entrepreneurs identify possibilities of smart specialization. The results of the analysis show country-specific differences in national startup profiles as well as variations among entrepreneurs coming from developed and less developed EU regions in terms of detecting business opportunities. Finally, we present policy implications for the European Smart Specialization Strategy. </div

Evaluation of the Implementation of Smart Specialisation Strategy in Lithuanian Industry

This article provides data-driven analyses of Lithuanian foreign trade activities. We combine Herfindahl-Hirschman Index (HHI) and Lauraéus-Kaivo-oja Index (LKI) measures to identify key changes and trends in export and import structures of the Lithuanian economy. The findings suggest that the export and import portfolios of the Lithuanian economy have been successfully diversified and the Lithuanian Smart Specialisation Strategy (S3) successful implemented in years 2015 through 2020. Presented in the form of HHI and LKI time series, our findings and the corresponding conclusions will be relevant to both the Lithuanian export and import industry and to industrial and economic policymakers in Lithuania and in international export and import agencies.</p

Mapping the Wave of Industry Digitalization by Co-Word Analysis: An Exploration of Four Disruptive Industries

This paper aims to identify global digital trends across industries and to map emerging business areas by co-word analysis. As the industrial landscape has become complex and dynamic due to the rapid pace of technological changes and digital transformation, identifying industrial trends can be critical for strategic planning and investment policy at the firm and regional level. For this purpose, the paper examines industry and technology profiles of top startups across four industries (i.e. education, finance, healthcare, manufacturing) using CrunchBase metadata for the period 2016-2018 and studies in which subsector early-stage firms bring digital technologies on a global level. In particular, we apply word co-occurrence analysis to reveal which subindustry and digital technology keywords/keyphrases appear together in startup company classification. We also use network analysis to visualize industry structure and to identify digitalization trends across sectors. The results obtained from the analysis show that gamification and personalization are emerging trends in the education sector. In the finance industry, digital technologies penetrate in a wide set of services such as financial transactions, payments, insurance, venture capital, stock exchange, asset and risk management. Moreover, the data analyses indicate that health diagnostics and elderly care areas are at the forefront of the healthcare industry digitalization. In the manufacturing sector, startup companies focus on automating industrial processes and creating smart interconnected manufacturing. Finally, we discuss the implications of the study for strategic planning and management

Human papillomavirus and post-transplant cutaneous squamous-cell carcinoma:a multicenter, prospective cohort study

Organ transplant recipients (OTRs) have a 100-fold increased risk of cutaneous squamous cell carcinoma (cSCC). We prospectively evaluated the association between β genus human papillomaviruses (βPV) and keratinocyte carcinoma in OTRs. Two OTR cohorts without cSCC were assembled: cohort 1 was transplanted in 2003-2006 (n =\ua0274) and cohort 2 was transplanted in 1986-2002 (n =\ua0352). Participants were followed until death or cessation of follow-up in 2016. βPV infection was assessed in eyebrow hair by using polymerase chain reaction-based methods. βPV IgG seroresponses were determined with multiplex serology. A competing risk model with delayed entry was used to estimate cumulative incidence of histologically proven cSCC and the effect of βPV by using a multivariable Cox regression model. Results are reported as adjusted hazard ratios (HRs). OTRs with 5 or more different βPV types in eyebrow hair had 1.7 times the risk of cSCC vs OTRs with 0 to 4 different types (HR 1.7, 95% confidence interval 1.1-2.6). A similar risk was seen with high βPV loads (HR 1.8, 95% confidence interval 1.2-2.8). No significant associations were seen between serum antibodies and cSCC or between βPV and basal cell carcinoma. The diversity and load of βPV types in eyebrow hair are associated with cSCC risk in OTRs, providing evidence that βPV is associated with cSCC carcinogenesis and may present a target for future preventive strategies