Gems of Corrado B\"ohm

The main scientific heritage of Corrado B\"ohm consists of ideas about computing, concerning concrete algorithms, as well as models of computability. The following will be presented. 1. A compiler that can compile itself. 2. Structured programming, eliminating the 'goto' statement. 3. Functional programming and an early implementation. 4. Separability in {\lambda}-calculus. 5. Compiling combinators without parsing. 6. Self-evaluation in {\lambda}-calculus

Gems of Corrado B\"ohm

The main scientific heritage of Corrado B\"ohm consists of ideas about computing, concerning concrete algorithms, as well as models of computability. The following will be presented. 1. A compiler that can compile itself. 2. Structured programming, eliminating the 'goto' statement. 3. Functional programming and an early implementation. 4. Separability in {\lambda}-calculus. 5. Compiling combinators without parsing. 6. Self-evaluation in {\lambda}-calculus

Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadMesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types (e.g., genomic, transcriptomic and metabolomic). These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim.Icelandic Research Fund Institute for Systems Biology's Translational Research Fellows Progra

Modelling metabolomic changes in human bone marrow derived mesenchymal stem cells during oesteogenic differentiation via genome scale metabolic models to further their use in regenerative medicine

Paper I in this thesis is under revision and may go through modification before official publication.Á undanförnum árum hafa sviðin endurnýjunar og tilfærslu lækningar vakið sífellt meiri áhuga vegna möguleikanna sem í þeim felast er varða nýjungar í læknavísindum. Innan þeirra má finna ýmis tól sem talin eru vera lykilatriði þegar kemur að því að finna og þróa ný meðferðarúrræði, og er eitt þessara tóla notkun mesenkýmal stofnfrumna (MSF). MSF hafa verið rannsakaðar m.t.t. ýmissa þátta þ.á.m. getu þeirra til að bæta endurnýjun beina og endurbyggingu beinvefs. Þrátt fyrir margvíslegar rannsóknir og meðfylgjandi framfarir í áttina að mögulegri klínískri notkun er margt sem er enn óljóst er varðar virkni og möguleika þessara frumna. Nákvæm og skipulögð rannsókn ásamt tiltölulega nákvæmum tölvulíkönum sem geta hermt eftir efnaskiptabreytingum og líkt eftir samfylgjandi svipgerðum gætu verið leiðir til að stoppa upp í núverandi göt í þekkingu hvað varðar efnaskiptabreytingar, frá tjáningu gena til framleiðslu próteina, ásamt því að veita möguleika á tilgátuprófunum án meðfylgjandi kostnaðar. Markmið verkefnisins sem hér er kynnt var að rannsaka efnaskiptabreytingar í mennskum MSF við beinsérhæfingu til að reyna að skilgreina mögulega efnaskipta fasa sem beinsérhæfingartímabilið væri samsett úr og búa til in silico módel byggð á genatengdum upplýsingum til að líkja eftir breytingunum. Í fyrsta hluta þessa verkefnis var notast við innan- og utanfrumu metabolómísk gögn til að skilgreina möguleg mismunandi stig beinsérhæfingar og hvaða efnaskiptabrautir geta legið þar að baki. Eftir greiningu gagna var lögð var fram tilgáta um tilveru þriggja mismunandi fasa og merkt innanfrumu gögn bentu til dvínandi virkni í glýkólýsu í gegnum sérhæfingartímann á sama tíma og aukningu gætti í hvatbera tengdum orkugefandi ferlum eftir því sem leið á. Þessar niðurstöður geta hjálpað með að finna tímapunkta sem eru af sérstökum áhuga er varðar kortlagningu mikilvægra efnaskiptabreytinga. Í öðrum hluta verkefnisins var notast við utanfrumu metabólómísk gögn sem safnað var frá mennskum MSF einangruðum úr beinmerg sem ræktaðar voru við venjulega skiptingu, fitu- og beinsérhæfingu, og þau notuð ásamt sértækum tilraunatengdum gögnum til að búa til þrjú sambærileg in silico módel, þau fyrstu sinnar gerðar, er líkja eftir efnaskiptabreytingum þessara þriggja frumugerða fyrstu 7 daga ræktunar. Módelin voru öll líffræðilega möguleg og sýndu af sér öll helstu einkennandi hvörf sem virk. Við greiningu og samanburð á módelunum fundust helstu breyttu undirkerfi og efnaskiptaferlar er voru einkennandi fyrir hverja frumugerð fyrir sig. Í tilviki frumna í venjulegri skiptingu voru breytt kerfi mjög fjölbreytt sem endurspeglar þær fjölbreyttu kröfur sem fruma þarf að uppfylla við að búa til annað eintak af sér, í tilviki fitusérhæfingar var nýmyndun og oxun fitusýra hvað mest áberandi og oxun fitusýra ásamt flutnings hvörfum einkenndu beinsérhæfingu. Þessi módel má nota til að sigta úr og setja upp ákjósanlegar tilraunir til að besta beinsérhæfingu og með breytingum mætti búa til sjúkdómsmódel til að líkja eftir beinþynningu. Í þriðja þætti verkefnisins sem hér er kynnt hafa verið tekið saman þau margvíslegu einkenni og fjölmörgu möguleikar er felast í MSF sem tóli til að nýta í endurnýjunar lækningum og vefjaverkfræði og hvernig, með notkun in silico efnaskiptamódela byggðum á genaupplýsingum, þá möguleika mætti þróa áfram á skilvirkari og hraðari máta samtímis því að lækka tilraunatengdan kostnað. Þetta verkefni hefur lagt sitt af mörkum er kemur að því að minnka núverandi eyðu þekkingar er varðar efnaskiptabreytingar er eiga sér stað í gegnum beinsérhæfingu mennskra MSF og hefur komið með að borðinu ný tól sem geta nýst til frekari rannsókna á sama sviði meðfram því að geta gert þær skilvirkari og hagkvæmari. hefur komið með að borðinu ný tól sem geta nýst til frekari rannsókna á sama sviði meðfram því að geta gert þær skilvirkari og hagkvæmari.In recent years the fields of regenerative and translational medicine have become the subjects of significantly growing interest due to their offer of previously unimaginable therapeutics. Within these fields are several novel tools believed to hold the keys to furthering existing and new developments and one of those tools is human mesenchymal stem cells. One of the applications hMSCs have been studied for is enhanced osteogenic regeneration or reconstruction of new bone tissue. Although various studies have been performed and some strides been made towards a plausible clinical application there is still lot left to be discovered. A methodical studying and relatively detailed in silico genome scale metabolic modelling occurring changes and the accompanying metabolic phenotypes could provide a means to fill in the existing knowledge gaps (from the protein level all the way to the genomic level) and, additionally, a means to perform hypotheses testing with a significant reduction when it comes to the accompanying cost. The objective of this thesis was to study the metabolomic changes in hMSCs during osteogenic differentiation using original transcriptomic, intracellular and extracellular metabolomic data in order try and define possible metabolic stages over the course of the differentiation and use genome scale network reconstruction to create in silico models. In the first part of this work extracellular and intracellular data were used to define possible stages to osteogenic differentiation and hypothesise which pathways may characterise the different metabolic phenotypes. Three stages were suggested based on the data and labelled intracellular metabolomics indicated a decrease in glycolytic dependencies throughout the differentiation period with an increase in mitochondria related energy producing functions as the osteogenesis progressed. This will help focus specific time points of interest and relevance when it comes to mapping the significant metabolomic changes. In the second part of this work extracellular metabolomic data collected from BM-hMSCs during proliferation, adipogenic and osteogenic differentiation was used along with experimentally specific data to create three directly comparable genome scale metabolic models, two of which have no comparable predecessors, for those cell lineages during the first 7 days of cell culture. Models were biologically feasible and showed all lineage specific characteristic reactions as active. By analysis and comparison, the various enriched subsystems and pathways most significant for each lineage were found. Results were varied for proliferating cells, which matches that they have to synthesise various metabolites and substances to expand, whilst fatty acid oxidation and fatty acid synthesis was most prominent in adipogenesis with fatty acid oxidation as well as transport reactions characteristic for osteogenesis. These models can be used for model-driven experimental design to engineer osteogenesis and, with modifications, to create disease model for osteoporosis. In the third part presented we summarized the various characteristics and possibilities that lie in using MSCs as a tool in tissue engineering and regenerative medicine and how, via implementation of genome scale metabolic model reconstruction, their possibilities could possibly be taken much further in a faster, more methodical manner while reducing the related experimental cost. To summarise, this work has provided real strides when it comes to closing the existing gap regarding metabolomic changes during differentiation of hMSCs as well as providing novel tools that can be used to make further studies more efficient and cost effective.The Icelandic research fund (grant number 217005)

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives