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Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly
Inspired by biology’s most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. Analogous high-dimensional, highly interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and genetic regulatory networks. Might collective modes analogous to neural computation be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles? Here we examine nucleation during self-assembly of multicomponent structures, showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computation. Specifically, we design a set of 917 DNA tiles that can self-assemble in three alternative ways such that competitive nucleation depends sensitively on the extent of colocalization of high-concentration tiles within the three structures. The system was trained in silico to classify a set of 18 grayscale 30 × 30 pixel images into three categories. Experimentally, fluorescence and atomic force microscopy measurements during and after a 150 hour anneal established that all trained images were correctly classified, whereas a test set of image variations probed the robustness of the results. Although slow compared to previous biochemical neural networks, our approach is compact, robust and scalable. Our findings suggest that ubiquitous physical phenomena, such as nucleation, may hold powerful information-processing capabilities when they occur within high-dimensional multicomponent systems
Using machine learning to predict pathogenicity of genomic variants throughout the human genome
Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität.
Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores.
Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt.
Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity.
Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants.
The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency.
In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
Apport de l’IRM structurelle multimodale dans la chirurgie d’épilepsie : le cas de l’épilepsie insulaire
L’épilepsie insulaire (ÉI) est une forme rare d’épilepsie focale qui, en raison des défis liés à son diagnostic, est difficilement cernable. De plus, la prise en charge des patients avec ÉI s’avère complexifiée par le fait que cette pathologie est fréquemment résistante aux médicaments anti-crises. Pour ces cas médico-réfractaires, la chirurgie insulaire est une option viable. Cela dit, les patients subissant une telle intervention développent fréquemment des déficits neurologiques postopératoires; heureusement, la grande majorité de ceux-ci récupèrent complètement et rapidement. Or, le mécanisme sous-tendant ce singulier rétablissement fonctionnel demeure à ce jour mal compris.
Deux modalités modernes d’IRM structurelle, soit l’analyse d’épaisseur corticale et la tractographie, ont permis, dans les dernières années, de décrire les altérations architecturales caractéristiques et potentiellement diagnostiques de divers types d’épilepsie ainsi que de caractériser les remodelages plastiques qui suivent la chirurgie de l’épilepsie extra-insulaire. Cependant, à ce jour, aucune étude ne s’est encore penchée sur le cas de l’ÉI. De ce fait, les études qui constituent cette thèse exploitent l’IRM structurelle afin, d’une part, de dépeindre les altérations d’épaisseur du cortex et de connectivité de matière blanche associées à l’ÉI et, d’autre part, de définir les réarrangements de connectivité subséquents à la chirurgie insulaire pour contrôle épileptique.
Les deux premières études de cette thèse ont révélé que l’ÉI était associée à un pattern majoritairement ipsilatéral d’atrophie corticale et d’hyperconnectivité impliquant principalement des sous-régions insulaires et des régions connectées à l’insula. De manière intéressante, la topologie de ces changements correspondait, au moins en partie, à celle du réseau épileptique de l’ÉI. Ensuite, la troisième étude visait à décrire, par le biais d’une méta-analyse, l’histoire naturelle postopératoire des patients subissant une chirurgie pour ÉI. Cette analyse a, entre autres, confirmé que cette chirurgie était efficace (66.7% de disparition des crises) et qu’elle était fréquemment accompagnée de complications neurologiques (42.5%) qui, dans la plupart des cas, étaient transitoires (78.7% des complications) et récupéraient entièrement dans les trois mois postopératoires (91.6% des complications transitoires). Finalement, la quatrième étude a révélé que la chirurgie pour ÉI était suivie d’altérations de connectivité diffuses et bilatérales. Notamment, les connexions présentant une augmentation de connectivité concernaient particulièrement des régions localisées soit près de la cavité chirurgicale ou dans l’hémisphère controlatéral à l’intervention. De plus, la majorité de ces renforcements structurels se sont produits dans les six premiers mois suivant la chirurgie, un délai comparable à celui durant lequel la majeure partie de la récupération fonctionnelle postopératoire a été observée dans notre méta-analyse.
En somme, nos résultats suggèrent que les altérations morphologiques en lien avec l’ÉI peuvent correspondre à son réseau épileptique sous-jacent. La topologie de ces changements pourrait constituer un biomarqueur structurel diagnostique qui aiderait à la reconnaissance de l’ÉI et, concomitamment, favoriserait possiblement un traitement chirurgical plus adapté et plus efficace. De plus, les augmentations de connectivité postopératoires pourraient correspondre à des réponses neuroplastiques permettant de prendre en charge les fonctions altérées par la chirurgie. Nos constats ont ainsi contribué à la caractérisation des mécanismes étayant la singulière récupération fonctionnelle accompagnant la chirurgie pour ÉI. À plus grande échelle, nos travaux offrent un aperçu du potentiel de l’IRM structurelle à assister au diagnostic de l’épilepsie focale ainsi qu’à participer à la description des changements plastiques subséquents à une résection neurochirurgicale.Insular epilepsy (IE) is a rare type of focal epilepsy that is difficult to diagnose. In addition to the challenging nature of IE detection, management of patients with this condition is complicated by the tendency of insular seizures to be resistant to anti-seizure medications. For such medically refractory cases, insular surgery constitutes a viable and long-lasting therapeutic option. That said, patients who undergo an insular resection for seizure control frequently develop postoperative neurological deficits; fortunately, most of these impairments recover fully and rapidly. While this favorable postoperative course contributes to improving the outcome of IE surgery, the mechanism underlying the functional recovery remains unknown.
Two contemporary structural MRI modalities, namely cortical thickness analysis and tractography, have recently been used to describe characteristic structural alterations of focal epilepsies and to elucidate the postoperative plastic remodeling associated with surgery for extra-insular epilepsy. While these analyses added to our understanding of several localization-related epilepsies, none specifically studied IE. In this thesis, we exploit structural MRI techniques to, first, depict the alterations of cortical thickness and white matter connectivity in IE and, second, define the progressive rearrangements that follow insular surgery for epilepsy.
The first two studies of the current thesis showed that IE is associated with a primarily ipsilateral pattern of cortical thinning and hyperconnectivity that mainly involves insular subregions and insula-connected regions. Interestingly, the topology of these changes corresponded, at least in part, to the epileptic network of IE. Furthermore, the third study aimed to describe, via a meta-analysis, the postoperative outcome of patients undergoing surgery for IE. Among other findings, the analysis revealed that insular surgery was effective (66.7% seizure freedom rate) but was associated with a significant risk of neurological complications (42.5%) which, in most cases, were transient (78.7% of all complications) and recovered fully within three months (91.6% of transient complications). Finally, the fourth study showed that surgery for IE was followed by a diffuse pattern of bilateral structural connectivity changes. Notably, connections exhibiting an increase in connectivity were specifically located near the surgical cavity and in the contralateral healthy hemisphere. In addition, the majority of the structural strengthening occurred in the first six months following surgery, a time course that is consistent with the short delay during which most of the postoperative functional recovery was observed in our meta-analysis.
Our results suggest that the morphological alterations in IE may reflect its underlying epileptic network. The topology of these changes may constitute a structural biomarker that could help diagnose IE more readily and, concomitantly, potentially enable a more targeted and more effective surgical treatment. Moreover, the postoperative increases in connectivity may be compatible with compensatory neuroplastic responses, a process that arose to recoup the functions of the injured insular cortex. Our findings have therefore contributed to the characterization of the driving process that supports the striking functional recovery seen following surgery for IE. On a larger scale, our work provides insights into the potential of structural MRI to assist in the diagnosis of focal epilepsy and to describe plastic changes following neurosurgical resections
Artificial Dendritic Neuron: A Model of Computation and Learning Algorithm
Dendrites are root-like extensions from the neuron cell body and have long been thought to serve as the predominant input structures of neurons. Since the early twentieth century, neuroscience research has attempted to define the dendrite’s contribution to neural computation and signal integration. This body of experimental and modeling research strongly indicates that dendrites are not just input structures but are crucial to neural processing. Dendritic processing consists of both active and passive elements that utilize the spatial, electrical and connective properties of the dendritic tree.
This work presents a neuron model based around the structure and properties of dendrites. This research assesses the computational benefits and requirements of adding dendrites to a spiking artificial neuron model. A list of the computational properties of actual dendrites that have shaped this work is given. An algorithm capable of generating and training a network of dendritic neurons is created as an investigative tool through which computational challenges and attributes are explored.
This work assumes that dendrites provide a necessary and beneficial function to biological intelligence (BI) and their translation into the artificial intelligence (AI) realm would broaden the capabilities and improve the realism of artificial neural network (ANN) research. To date there have been only a few instances in which neural network-based AI research has ventured beyond the point neuron; therefore, the work presented here should be viewed as exploratory. The contribution to AI made by this work is an implementation of the artificial dendritic (AD) neuron model and an algorithm for training AD neurons with spatially distributed inputs with dendrite-like connectivity
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
Evolution from the ground up with Amee – From basic concepts to explorative modeling
Evolutionary theory has been the foundation of biological research for about a century
now, yet over the past few decades, new discoveries and theoretical advances have rapidly
transformed our understanding of the evolutionary process. Foremost among them are
evolutionary developmental biology, epigenetic inheritance, and various forms of evolu-
tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led
to the conceptualization of an extended evolutionary synthesis. Starting from abstract
principles rooted in complexity theory, this thesis aims to provide a unified conceptual
understanding of any kind of evolution, biological or otherwise. This is used in the second
part to develop Amee, an agent-based model that unifies development, niche construction,
and phenotypic plasticity with natural selection based on a simulated ecology. Amee
is implemented in Utopia, which allows performant, integrated implementation and
simulation of arbitrary agent-based models. A phenomenological overview over Amee’s
capabilities is provided, ranging from the evolution of ecospecies down to the evolution
of metabolic networks and up to beyond-species-level biological organization, all of
which emerges autonomously from the basic dynamics. The interaction of development,
plasticity, and niche construction has been investigated, and it has been shown that while
expected natural phenomena can, in principle, arise, the accessible simulation time and
system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes
Self-Learning Longitudinal Control for On-Road Vehicles
Fahrerassistenzsysteme (Advanced Driver Assistance Systems) sind ein wichtiges Verkaufsargument fĂĽr PKWs, fordern jedoch hohe Entwicklungskosten.
Insbesondere die Parametrierung für Längsregelung, die einen wichtigen Baustein für Fahrerassistenzsysteme darstellt, benötigt viel Zeit und Geld, um die richtige Balance zwischen Insassenkomfort und Regelgüte zu treffen.
Reinforcement Learning scheint ein vielversprechender Ansatz zu sein, um dies zu automatisieren.
Diese Klasse von Algorithmen wurde bislang allerdings vorwiegend auf simulierte Aufgaben angewendet, die unter idealen Bedingungen stattfinden und nahezu unbegrenzte Trainingszeit ermöglichen.
Unter den größten Herausforderungen für die Anwendung von Reinforcement Learning in einem realen Fahrzeug sind Trajektorienfolgeregelung und unvollständige Zustandsinformationen aufgrund von nur teilweise beobachteter Dynamik.
DarĂĽber hinaus muss ein Algorithmus, der in realen Systemen angewandt wird, innerhalb von Minuten zu einem Ergebnis kommen.
Außerdem kann das Regelziel sich während der Laufzeit beliebig ändern, was eine zusätzliche Schwierigkeit für Reinforcement Learning Methoden darstellt.
Diese Arbeit stellt zwei Algorithmen vor, die wenig Rechenleistung benötigen und diese Hürden überwinden.
Einerseits wird ein modellfreier Reinforcement Learning Ansatz vorgeschlagen, der auf der Actor-Critic-Architektur basiert und eine spezielle Struktur in der Zustandsaktionswertfunktion verwendet, um mit teilweise beobachteten Systemen eingesetzt werden zu können.
Um eine Vorsteuerung zu lernen, wird ein Regler vorgeschlagen, der sich auf eine Projektion und Trainingsdatenmanipulation stĂĽtzt.
Andererseits wird ein modellbasierter Algorithmus vorgeschlagen, der auf Policy Search basiert.
Diesem wird eine automatisierte Entwurfsmethode fĂĽr eine inversionsbasierte Vorsteuerung zur Seite gestellt.
Die vorgeschlagenen Algorithmen werden in einer Reihe von Szenarien verglichen, in denen sie online, d.h. während der Fahrt und bei geschlossenem Regelkreis, in einem realen Fahrzeug lernen.
Obwohl die Algorithmen etwas unterschiedlich auf verschiedene Randbedingungen reagieren, lernen beide robust und zügig und sind in der Lage, sich an verschiedene Betriebspunkte, wie zum Beispiel Geschwindigkeiten und Gänge, anzupassen, auch wenn Störungen während des Trainings einwirken.
Nach bestem Wissen des Autors ist dies die erste erfolgreiche Anwendung eines Reinforcement Learning Algorithmus, der online in einem realen Fahrzeug lernt
Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running
Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products
Machine Learning and Its Application to Reacting Flows
This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation
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