91 research outputs found
Brain Computations and Connectivity [2nd edition]
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations.
Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed.
The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes.
Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions.
This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press.
Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
LIPIcs, Volume 244, ESA 2022, Complete Volume
LIPIcs, Volume 244, ESA 2022, Complete Volum
Explanatory Models, Unit Standards, and Personalized Learning in Educational Measurement
The papers by Jack Stenner included in this book document the technical details of an art and science of measurement that creates new entrepreneurial business opportunities. Jack brought theory, instruments, and data together in ways that are applicable not only in the context of a given test of reading or mathematics ability, but which more importantly catalyzed literacy and numeracy capital in new fungible expressions. Though Jack did not reflect in writing on the inferential, constructive processes in which he engaged, much can be learned by reviewing his work with his accomplishments in mind. A Foreword by Stenner's colleague and co-author on multiple works, William P. Fisher, Jr., provides key clues concerning (a) how Jack's understanding of measurement and its values aligns with social and historical studies of science and technology, and (b) how recent developments in collaborations of psychometricians and metrologists are building on and expanding Jack's accomplishments. ​ This is an open access book
Evolutionary alternatives examined with three examples: Amino acids, coiled coils and strategies of iron-cycling bacteria
The questions of why things are the way they are and if they could have been any different are frequently occupying the minds of us human beings. One way out is provided by the assumption of contingency, the view that long-term development is mainly dependent on the results of many random events. However, I argue that from a scientific point of view, fundamentally revolving around skepticism and the search for underlying patterns, contingency does not provide a comfortable answer and should always be perceived as a preliminary resort. This thesis revolves around the investigation of evolutionary alternatives related to case studies at three different levels of biological complexity. Key aspects of evolutionary alternatives are the pool of available elements to choose from, the pressures which lead to the preference for the selection of certain choices over others, and the conditions under which these selections comprise a viable or even optimal choice for the organism(s)
Mining of soluble enzymes from genomic databases
Enzymy jsou proteiny urychlujĂcĂ chemickĂ© reakce s velkĂ˝m potenciálem pro farmaceutickĂ˝ a obecnÄ› chemickĂ˝ prĹŻmysl. Enzymatická funkce je obvykle zajištÄ›na nÄ›kolika nepostradatelnĂ˝mi aminokyselinami, kterĂ© tvořà tzv. aktivnĂ mĂsto, kde se odehrává chemická reakce. V tĂ©to práci jsou prezentovány dva integrovanĂ© softwarovĂ© nástroje pro dolovánĂ a racionálnĂ vĂ˝bÄ›r novĂ˝ch rozpustnĂ˝ch enzymĹŻ - EnzymeMiner a SoluProt. EnzymeMiner sloužà k hledánĂ novĂ˝ch enzymĹŻ. Na vstupu vyĹľaduje jednu nebo vĂce sekvencĂ zvolenĂ©ho enzymu spolu se seznamem klĂÄŤovĂ˝ch aminokyselin. Tento seznam sloužà k zvýšenĂ pravdÄ›podobnosti, Ĺľe nalezenĂ˝ enzym bude mĂt podobnou funkci jako vstupnĂ enzym. VĂ˝stupem EnzymeMineru je mnoĹľina anotovanĂ˝ch sekvencĂ nalezenĂ˝ch v databázi. Za účelem ulehÄŤenĂ vĂ˝bÄ›ru nÄ›kolika málo kandidátĹŻ pro experimentálnĂ ověřenĂ v laboratoĹ™i integruje EnzymeMiner anotace z dostupnĂ˝ch databázĂ - informaci o zdrojovĂ©m organismu a prostĹ™edĂ, ve kterĂ©m se vyskytuje, a informaci o proteinovĂ˝ch domĂ©nách, ze kterĂ˝ch se enzym skládá. HlavnĂm kritĂ©riem pro vĂ˝bÄ›r kandidátĹŻ je rozpustnost predikovaná druhĂ˝m prezentovanĂ˝m nástrojem, SoluProtem. SoluProt je metoda zaloĹľená na strojovĂ©m uÄŤenĂ, která predikuje heterolognĂ rozpustnou expresi proteinu v organismu Escherichia coli . Vstupem je sekvence a vĂ˝stupem je pravdÄ›podobnost, Ĺľe protein bude exprimován v rozpustnĂ© formÄ›. SoluProt vyuĹľĂvá model gradient boosting machine a byl trĂ©nován na datovĂ© sadÄ› odvozenĂ© od databáze TargetTrack. PĹ™i srovnánĂ na vyváženĂ© nezávislĂ© datovĂ© sadÄ› odvozenĂ© z databáze NESG dosáhl SoluProt pĹ™esnosti 58,5 % a hodnoty AUC 0,62, ÄŤĂmĹľ lehce pĹ™evyšuje ostatnĂ existujĂcĂ nástroje. Nástroje EnzymeMiner i SoluProt jsou ÄŤasto vyuĹľĂvány Ĺ™adou uĹľivatelĹŻ z oblasti proteinovĂ©ho inĹľenĂ˝rstvĂ za účelem hledánĂ novĂ˝ch rozpustnĂ˝ch biokatalyzátorĹŻ chemickĂ˝ch reakcĂ. Ty majĂ velkĂ˝ potenciál snĂĹľit energetickou nároÄŤnost a ekologickou zátěž mnoha prĹŻmyslovĂ˝ch procesĹŻ.Enzymes are proteins accelerating chemical reactions, which makes them attractive targets for both pharmaceutical and industrial applications. The enzyme function is mediated by several essential amino acids which form the optimal chemical environment to catalyse the reaction. In this work, two integrated bioinformatics tools for mining and rational selection of novel soluble enzymes, EnzymeMiner and SoluProt, are presented. EnzymeMiner uses one or more enzyme sequences as input along with a description of essential residues to search the protein database. The description of essential amino acids is used to increase the probability of similar enzymatic function. EnzymeMiner output is a set of annotated database hits. EnzymeMiner integrates taxonomic, environmental, and protein domain annotations to facilitate selection of promising targets for experiments. The main prioritization criterion is solubility predicted by the second tool being presented, SoluProt. SoluProt is a machine-learning method for the prediction of soluble protein expression in Escherichia coli . The input is a protein sequence and the output is the probability of such protein to be soluble. SoluProt exploits a gradient boosting machine to decide on the output prediction class. The tool was trained on TargetTrack database. When evaluated against a balanced independent test set derived from the NESG database, SoluProt accuracy was 58.5% and its AUC 0.62, slightly exceeding those of a suite of alternative solubility prediction tools. Both EnzymeMiner and SoluProt are frequently used by the protein engineering community to find novel soluble biocatalysts for chemical reactions. These have a great potential to decrease energetic consumption and environmental burden of many industrial chemical processes.
Resolving tricky nodes in the tree of life through amino acid recoding
Genomic data allowed a detailed resolution of the Tree of Life, but ''tricky nodes'' such as the root of the animals remain unresolved. Genome-scale datasets are heterogeneous as genes and species are exposed to different pressures, and this can negatively impacts phylogenetic accuracy. We use simulated genomic- scale datasets and show that recoding amino acid data improves accuracy when the model does not account for the compositional heterogeneity of the amino acid alignment. We apply our findings to three datasets addressing the root of the animal tree, where the debate centers on whether sponges (Porifera) or comb jellies (Ctenophora) represent the sister of all other animals. We show that results from empirical data follow predictions from simulations and suggest that, at the least in phylogenies inferred from amino acid sequences, a placement of the ctenophores as sister to all the other animals is best explained as a tree reconstruction artifact
The Functioning of Ecosystems
The ecosystems present a great diversity worldwide and use various functionalities according to ecologic regions. In this new context of variability and climatic changes, these ecosystems undergo notable modifications amplified by domestic uses of which it was subjected to. Indeed the ecosystems render diverse services to humanity from their composition and structure but the tolerable levels are unknown. The preservation of these ecosystemic services needs a clear understanding of their complexity. The role of the research is not only to characterise the ecosystems but also to clearly define the tolerable usage levels. Their characterisation proves to be important not only for the local populations that use it but also for the conservation of biodiversity. Hence, the measurement, management and protection of ecosystems need innovative and diverse methods. For all these reasons, the aim of this book is to bring out a general view on the biogeochemical cycles, the ecological imprints, the mathematical models and theories applicable to many situations
Statistical methods for biological sequence analysis for DNA binding motifs and protein contacts
Over the last decades a revolution in novel measurement techniques has permeated the biological sciences filling the databases with unprecedented amounts of data ranging from genomics, transcriptomics, proteomics and metabolomics to structural and ecological data. In order to extract insights from the vast quantity of data, computational and statistical methods are nowadays crucial tools in the toolbox of every biological researcher. In this thesis I summarize my contributions in two data-rich fields in biological sciences: transcription factor binding to DNA and protein structure prediction from protein sequences with shared evolutionary ancestry.
In the first part of my thesis I introduce our work towards a web server for analysing transcription factor binding data with Bayesian Markov Models. In contrast to classical PWM or di-nucleotide models, Bayesian Markov models can capture complex inter-nucleotide dependencies that can arise from shape-readout and alternative binding modes. In addition to giving access to our methods in an easy-to-use, intuitive web-interface, we provide our users with novel tools and visualizations to better evaluate the biological relevance of the inferred binding motifs. We hope that our tools will prove useful for investigating weak and complex transcription factor binding motifs which cannot be predicted accurately with existing tools.
The second part discusses a statistical attempt to correct out the phylogenetic bias arising in co-evolution methods applied to the contact prediction problem. Co-evolution methods have revolutionized the protein-structure prediction field more than 10 years ago, and, until very recently, have retained their importance as crucial input features to deep neural networks. As the co-evolution information is extracted from evolutionarily related sequences, we investigated whether the phylogenetic bias to the signal can be corrected out in a principled way using a variation of the Felsenstein's tree-pruning algorithm applied in combination with an independent-pair assumption to derive pairwise amino counts that are corrected for the evolutionary history. Unfortunately, the contact prediction derived from our corrected pairwise amino acid counts did not yield a competitive performance.2021-09-2
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