The 1990 Goddard Conference on Space Applications of Artificial Intelligence

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition

Laskennallisia menetelmiä säilyneiden geenisäätelyelementtien analyysiin ja paikallistamiseen DNA:sta

This thesis presents methods for locating and analyzing cis-regulatory DNA elements involved with the regulation of gene expression in multicellular organisms. The regulation of gene expression is carried out by the combined effort of several transcription factor proteins collectively binding the DNA on the cis-regulatory elements. Only sparse knowledge of the 'genetic code' of these elements exists today. An automatic tool for discovery of putative cis-regulatory elements could help their experimental analysis, which would result in a more detailed view of the cis-regulatory element structure and function. We have developed a computational model for the evolutionary conservation of cis-regulatory elements. The elements are modeled as evolutionarily conserved clusters of sequence-specific transcription factor binding sites. We give an efficient dynamic programming algorithm that locates the putative cis-regulatory elements and scores them according to the conservation model. A notable proportion of the high-scoring DNA sequences show transcriptional enhancer activity in transgenic mouse embryos. The conservation model includes four parameters whose optimal values are estimated with simulated annealing. With good parameter values the model discriminates well between the DNA sequences with evolutionarily conserved cis-regulatory elements and the DNA sequences that have evolved neutrally. In further inquiry, the set of highest scoring putative cis-regulatory elements were found to be sensitive to small variations in the parameter values. The statistical significance of the putative cis-regulatory elements is estimated with the Two Component Extreme Value Distribution. The p-values grade the conservation of the cis-regulatory elements above the neutral expectation. The parameter values for the distribution are estimated by simulating the neutral DNA evolution. The conservation of the transcription factor binding sites can be used in the upstream analysis of regulatory interactions. This approach may provide mechanistic insight to the transcription level data from, e.g., microarray experiments. Here we give a method to predict shared transcriptional regulators for a set of co-expressed genes. The EEL (Enhancer Element Locator) software implements the method for locating putative cis-regulatory elements. The software facilitates both interactive use and distributed batch processing. We have used it to analyze the non-coding regions around all human genes with respect to the orthologous regions in various other species including mouse. The data from these genome-wide analyzes is stored in a relational database which is used in the publicly available web services for upstream analysis and visualization of the putative cis-regulatory elements in the human genome.Kun ihmisen genomi saatiin sekvensoitua eli ihmisen geenit oli löydetty ja eritelty vuosituhannen alussa, tiedemiehet yllättyivät ihmisen geenien pienestä määrästä. Ihmisellä havaittiin olevan vain vähän enemmän geenejä kuin yksinkertaisella sukkulamadolla. Koska geenien lukumäärä ei pystykään selittämään ihmisen ja sukkulamadon ulkoisia eroavaisuuksia, selitystä ruvettiin etsimään geenien toiminnan eroista. Geenien toimintaa säädellään monisoluisissa eliöissä hyvin tarkasti tiettyyn paikkaan ja tiettyyn osaan ruumista. Tietyt proteiinit toteuttavat geenien säätelyä sitoutumalla tiettyihin kohtiin DNA:ta säädeltävän geenin läheisyydessä. Näiden DNA:han sitoutumiskohtien löytäminen genomista on kokeellisesti hyvin haastavaa: ne saattavat sijaita hyvin kaukana säädeltävästä geenistä eikä proteiinien sitoutumissääntöjä tunneta vielä kovin hyvin. Väitöstyössä on kehitetty laskennallisia menetelmiä geenisäätelyyn liittyvien DNA sitoutumiskohtien paikantamiseen eri nisäkkäiden genomeja vertailemalla. Esimerkiksi ihmisen ja hiiren genomeja vertailemalla voidaan paikantaa DNA:n pätkiä, jotka ovat olleet hiirien ja ihmisten viimeisessä yhteisessä esivanhemmassa noin 65 miljoonaa vuotta sitten ja lisäksi vaikuttavat mahdollisilta proteiinien sitoutumiskohdilta. Tällaisia mahdollisia DNA:han sitoutumiskohtia on löydetty ihmisen genomista tuhansia, ja osan niistä on kokeellisesti havaittu säätelevän lähellä sijaitsevaa geeniä. Sitoutumiskohtien analysointiin kehitettiin väitöstutkimuksessa menetelmä, jolla voidaan ennustaa geenijoukoille säätelyproteiineja. Nykyaikaiset tehoseulontamenetelmät löytävät nopeasti geenijoukkoja, joilla on jokin kiinnostava ominaisuus, jonka säätelystä ollaan kiinnostuneita. Kehitetyllä menetelmällä voidaan helposti ennustaa esimerkiksi tiettyyn sairauteen liittyvien geenien säätelijä. Kun mahdollinen säätelijäproteiini tunnetaan, sitä vastaan voidaan kehittää lääke. Työn tulokset antavat uusia menetelmiä erityisesti vaikeasti tutkittavien yksilönkehityksen aikana säädeltyjen geenien analyysiin. Kehitettyjen menetelmien lääketieteelliset sovellukset liittyvät esimerkiksi kudosspesifiin kasvun säätelyyn ja syöpägeenien kasvainspesifisyyteen. Nämä sovellukset pyrkivät selvittämään mm. syytä ihmisen suhteettoman suurille aivoille ja pienille lihaksille ja toisaalta pyrkivät avaamaan uusia lähestymistapoja esimerkiksi syövän diagnostiikkaan ja hoitoon

The 1992 Goddard Conference on Space Applications of Artificial Intelligence

The purpose of this conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers fall into the following areas: planning and scheduling, control, fault monitoring/diagnosis and recovery, information management, tools, neural networks, and miscellaneous applications

Higher-order interactions in single-cell gene expression: towards a cybergenetic semantics of cell state

Finding and understanding patterns in gene expression guides our understanding of living organisms, their development, and diseases, but is a challenging and high-dimensional problem as there are many molecules involved. One way to learn about the structure of a gene regulatory network is by studying the interdependencies among its constituents in transcriptomic data sets. These interdependencies could be arbitrarily complex, but almost all current models of gene regulation contain pairwise interactions only, despite experimental evidence existing for higher-order regulation that cannot be decomposed into pairwise mechanisms. I set out to capture these higher-order dependencies in single-cell RNA-seq data using two different approaches. First, I fitted maximum entropy (or Ising) models to expression data by training restricted Boltzmann machines (RBMs). On simulated data, RBMs faithfully reproduced both pairwise and third-order interactions. I then trained RBMs on 37 genes from a scRNA-seq data set of 70k astrocytes from an embryonic mouse. While pairwise and third-order interactions were revealed, the estimates contained a strong omitted variable bias, and there was no statistically sound and tractable way to quantify the uncertainty in the estimates. As a result I next adopted a model-free approach. Estimating model-free interactions (MFIs) in single-cell gene expression data required a quasi-causal graph of conditional dependencies among the genes, which I inferred with an MCMC graph-optimisation algorithm on an initial estimate found by the Peter-Clark algorithm. As the estimates are model-free, MFIs can be interpreted either as mechanistic relationships between the genes, or as substructures in the cell population. On simulated data, MFIs revealed synergy and higher-order mechanisms in various logical and causal dynamics more accurately than any correlation- or information-based quantities. I then estimated MFIs among 1,000 genes, at up to seventh-order, in 20k neurons and 20k astrocytes from two different mouse brain scRNA-seq data sets: one developmental, and one adolescent. I found strong evidence for up to fifth-order interactions, and the MFIs mostly disambiguated direct from indirect regulation by preferentially coupling causally connected genes, whereas correlations persisted across causal chains. Validating the predicted interactions against the Pathway Commons database, gene ontology annotations, and semantic similarity, I found that pairwise MFIs contained different but a similar amount of mechanistic information relative to networks based on correlation. Furthermore, third-order interactions provided evidence of combinatorial regulation by transcription factors and immediate early genes. I then switched focus from mechanism to population structure. Each significant MFI can be assigned a set of single cells that most influence its value. Hierarchical clustering of the MFIs by cell assignment revealed substructures in the cell population corresponding to diverse cell states. This offered a new, purely data-driven view on cell states because the inferred states are not required to localise in gene expression space. Across the four data sets, I found 69 significant and biologically interpretable cell states, where only 9 could be obtained by standard approaches. I identified immature neurons among developing astrocytes and radial glial cells, D1 and D2 medium spiny neurons, D1 MSN subtypes, and cell-cycle related states present across four data sets. I further found evidence for states defined by genes associated to neuropeptide signalling, neuronal activity, myelin metabolism, and genomic imprinting. MFIs thus provide a new, statistically sound method to detect substructure in single-cell gene expression data, identifying cell types, subtypes, or states that can be delocalised in gene expression space and whose hierarchical structure provides a new view on the semantics of cell state. The estimation of the quasi-causal graph, the MFIs, and inference of the associated states is implemented as a publicly available Nextflow pipeline called Stator

RNA, the Epicenter of Genetic Information

The origin story and emergence of molecular biology is muddled. The early triumphs in bacterial genetics and the complexity of animal and plant genomes complicate an intricate history. This book documents the many advances, as well as the prejudices and founder fallacies. It highlights the premature relegation of RNA to simply an intermediate between gene and protein, the underestimation of the amount of information required to program the development of multicellular organisms, and the dawning realization that RNA is the cornerstone of cell biology, development, brain function and probably evolution itself. Key personalities, their hubris as well as prescient predictions are richly illustrated with quotes, archival material, photographs, diagrams and references to bring the people, ideas and discoveries to life, from the conceptual cradles of molecular biology to the current revolution in the understanding of genetic information. Key Features Documents the confused early history of DNA, RNA and proteins - a transformative history of molecular biology like no other. Integrates the influences of biochemistry and genetics on the landscape of molecular biology. Chronicles the important discoveries, preconceptions and misconceptions that retarded or misdirected progress. Highlights major pioneers and contributors to molecular biology, with a focus on RNA and noncoding DNA. Summarizes the mounting evidence for the central roles of non-protein-coding RNA in cell and developmental biology. Provides a thought-provoking retrospective and forward-looking perspective for advanced students and professional researchers

Renewable Energy

This book discusses renewable energy resources and systems as well as energy efficiency. It contains twenty-three chapters over six sections that address a multitude of renewable energy types, including solar and photovoltaic, biomass, hydroelectric, and geothermal. The information presented herein is a scientific contribution to energy and environmental regulations, quality and efficiency of energy services, energy supply security, energy market-based approaches, government interventions, and the spread of technological innovation

Critical Thinking Skills Profile of High School Students In Learning Science-Physics

This study aims to describe Critical Thinking Skills high school students in the city of Makassar. To achieve this goal, the researchers conducted an analysis of student test results of 200 people scattered in six schools in the city of Makassar. The results of the quantitative descriptive analysis of the data found that the average value of students doing the interpretation, analysis, and inference in a row by 1.53, 1.15, and 1.52. This value is still very low when compared with the maximum value that may be obtained by students, that is equal to 10.00. This shows that the critical thinking skills of high school students are still very low. One fact Competency Standards science subjects-Physics is demonstrating the ability to think logically, critically, and creatively with the guidance of teachers and demonstrate the ability to solve simple problems in daily life. In fact, according to Michael Scriven stated that the main task of education is to train students and or students to think critically because of the demands of work in the global economy, the survival of a democratic and personal decisions and decisions in an increasingly complex society needs people who can think well and make judgments good. Therefore, the need for teachers in the learning device scenario such as: driving question or problem, authentic Investigation: Science Processes