Rarity: Discovering rare cell populations from single-cell imaging data

MOTIVATION: Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori. While unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that are magnified when they are defined by differentially expressing a small number of genes. RESULTS: Typical unsupervised approaches fail to identify such rare sub-populations, and these cells tend to be absorbed into more prevalent cell types. In order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries. We systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC data sets. AVAILABILITY: Implementation of Rarity together with examples are available from the Github repository (https://github.com/kasparmartens/rarity). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Plasma Metabolome Alterations Discriminate between COVID-19 and Non-COVID-19 Pneumonia

Pneumonia is a common cause of morbidity and mortality and is most often caused by bacterial pathogens. COVID-19 is characterized by lung infection with potential progressive organ failure. The systemic consequences of both disease on the systemic blood metabolome are not fully understood. The aim of this study was to compare the blood metabolome of both diseases and we hypothesize that plasma metabolomics may help to identify the systemic effects of these diseases. Therefore, we profiled the plasma metabolome of 43 cases of COVID-19 pneumonia, 23 cases of non-COVID-19 pneumonia, and 26 controls using a non-targeted approach. Metabolic alterations differentiating the three groups were detected, with specific metabolic changes distinguishing the two types of pneumonia groups. A comparison of venous and arterial blood plasma samples from the same subjects revealed the distinct metabolic effects of pulmonary pneumonia. In addition, a machine learning signature of four metabolites was predictive of the disease outcome of COVID-19 subjects with an area under the curve (AUC) of 86 ± 10 %. Overall, the results of this study uncover systemic metabolic changes that could be linked to the etiology of COVID-19 pneumonia and nonCOVID-19 pneumonia

First 230 GHz VLBI Fringes on 3C 279 using the APEX Telescope

We report about a 230 GHz very long baseline interferometry (VLBI) fringe finder observation of blazar 3C 279 with the APEX telescope in Chile, the phased submillimeter array (SMA), and the SMT of the Arizona Radio Observatory (ARO). We installed VLBI equipment and measured the APEX station position to 1 cm accuracy (1 sigma). We then observed 3C 279 on 2012 May 7 in a 5 hour 230 GHz VLBI track with baseline lengths of 2800 M$\lambda$ to 7200 M$\lambda$ and a finest fringe spacing of 28.6 micro-arcseconds. Fringes were detected on all baselines with SNRs of 12 to 55 in 420 s. The correlated flux density on the longest baseline was ~0.3 Jy/beam, out of a total flux density of 19.8 Jy. Visibility data suggest an emission region <38 uas in size, and at least two components, possibly polarized. We find a lower limit of the brightness temperature of the inner jet region of about 10^10 K. Lastly, we find an upper limit of 20% on the linear polarization fraction at a fringe spacing of ~38 uas. With APEX the angular resolution of 230 GHz VLBI improves to 28.6 uas. This allows one to resolve the last-photon ring around the Galactic Center black hole event horizon, expected to be 40 uas in diameter, and probe radio jet launching at unprecedented resolution, down to a few gravitational radii in galaxies like M 87. To probe the structure in the inner parsecs of 3C 279 in detail, follow-up observations with APEX and five other mm-VLBI stations have been conducted (March 2013) and are being analyzed.Comment: accepted for publication in A&

Divisible E-Cash from Constrained Pseudo-Random Functions

International audienceElectronic cash (e-cash) is the digital analogue of regular cash which aims at preservingusers’ privacy. Following Chaum’s seminal work, several new features were proposed for e-cash toaddress the practical issues of the original primitive. Among them,divisibilityhas proved very usefulto enable efficient storage and spendings. Unfortunately, it is also very difficult to achieve and, todate, quite a few constructions exist, all of them relying on complex mechanisms that can only beinstantiated in one specific setting. In addition security models are incomplete and proofs sometimeshand-wavy.In this work, we first provide a complete security model for divisible e-cash, and we study the linkswith constrained pseudo-random functions (PRFs), a primitive recently formalized by Boneh andWaters. We exhibit two frameworks of divisible e-cash systems from constrained PRFs achievingsome specific properties: either key homomorphism or delegability. We then formally prove theseframeworks, and address two main issues in previous constructions: two essential security notionswere either not considered at all or not fully proven. Indeed, we introduce the notion ofclearing,which should guarantee that only the recipient of a transaction should be able to do the deposit,and we show theexculpability, that should prevent an honest user to be falsely accused, was wrongin most proofs of the previous constructions. Some can easily be repaired, but this is not the casefor most complex settings such as constructions in the standard model. Consequently, we providethe first construction secure in the standard model, as a direct instantiation of our framework

Information Propagation in Complex Networks: Structures and Dynamics

This thesis is a contribution to a deeper understanding of how information propagates and what this process entails. At its very core is the concept of the network: a collection of nodes and links, which describes the structure of the systems under investigation. The network is a mathematical model which allows to focus on a very fundamental property: the mutual relations (links) between information exchanging agents (nodes). This simplicity makes networks elegant, as no specifics of any supporting hardware are needed to reason on this high level of abstraction. The developing field of network science led to countless applications of the network model to all sorts of complex systems in nature and technology. Naturally, it became an essential part of many multi-disciplinary research projects. Therefore, understanding how information propagates in networks enables us to learn and conceivably control the intricate processes, which we observe in complex systems. Since complex systems are the driver for this research, the first three chapters of this thesis are studies based on data collected from vastly different application domains, after more fundamental research is addressed in the later parts.Chapter 2 deals with the interaction of players of a popular multiplayer online game. Due to the competitive design of the game, teams are formed ad-hoc and compete with each other for victory. Some of the players exhibit anti-social behavior towards their teammates, which is known as toxicity. We analyze how toxicity in player networks emerges by developing a toxicity detector, highlighting possible triggers and analyze the disposition of players towards toxic teammates. Furthermore, we show how toxicity is linked to game success.Chapter 3 continues with a study of the human brain as a functional network. Information processing in the brain is measurable with technologies like magnetoencephalography. From such measurements that were collected from a group of subjects, the phase transfer entropy is computed as a quantity that reflects information exchange. When associated with the links between brain regions, unusual high numbers of certain substructures are observed in this network. We find that one of these substructures, the bi-directional two-hop path, to be highly abundant and robust within different frequencies bands, which highlights its importance for the propagation of brain activity. A clustering of the network based on these frequent substructures reveals a spatially coherent organization of important brain regions.A common symbol of propagation is the virus, which is at the center of the third data-driven analysis of this thesis in Chapter 4. More precisely, we research the digital version of the virus, the computer worm, and analyze its propagation by epidemic network models. With epidemic models, the state of the nodes in a network can be described as susceptible or infected. An infection process and a curing process determine how the nodes are changing between those states. We extend on the standard epidemic models, the SIS model, by a time-dependent curing rate function to reflect the changes in the effectiveness of the active worm removal. Once we set the curing rate function, the empirical worm data are fitted and analyzed on multiple scales from the global over the country down to the autonomous system level. The fitted model explains how computer worms or similar self-replicating pieces of information might change in their effectiveness over long periods of time.The SIS model returns as a central piece in Chapter 5 again. Although spreading processes are frequently modeled in isolation, the dynamics of many real-world applications are often driven by the interaction of multiple of such processes. These interactions can range from viruses that compete for susceptible nodes to viruses that mutually reinforce their propagation. We study the special case of superinfection, in which one dominant virus spreads within the infected population of a weaker virus. We highlight the conditions for which a co-existence of both viruses is stable and show that extinction cycles become possible if the infection rate of the dominant virus becomes too strong. Furthermore, we show that some of the possible outcomes of a superinfection are difficult to approximate with common mean-field techniques. However, the second largest eigenvalue of the infinitesimal generator of the underlying Markov process is potentially linked to co-existence and thus stability.Chapter 6 is a study on the capabilities of symbolic regression for network properties. We develop an automated system based on Genetic Programming which is able to be trained by families of networks to learn the relations between several of their properties. These properties can be features of the networks like the eigenvalues of their adjacency or Laplacian matrices or network metrics like the network diameter or the isoperimetric number. We show that the system can generate approximate formulas for those metrics that often give better results than previously known analytic bounds. The evolved formulas for the network diameter are evaluated on a selection of real-world networks of different origins. The network diameter bounds hop-based information propagation and is thus of high importance for designing network algorithms. A careful selection of training networks and network features is crucial for evolving good approximate formulas for the network diameter and similar properties.Finally, the thesis concludes with Chapter 7 which revisits the concepts that were developed and provides some critical assessment on their potential and limitations.Network Architectures and Service

Vibration Control Of A Mechanical Structure With Piezoelectric Actuators - A Comparison Of Bimorph And Stack Actuators

: The practical results of the vibration suppression of a cantilever beam with a low voltage piezoelectric bimorph patch and a stack actuator are discussed. After the equation of motion of the controlled system is obtained by the finite element method, a modal analysis is performed to reduce the numbers of degrees of freedom. A modal linear quadratic feedback controller is realized on a real-time digital control system to damp the first five modes of the mechanical structure. Optimal placement of the actuators is considered by the mode shapes and strain distribution. Comparison of the time history of an impulse excitation and the frequency spectrum shows the control effectiveness of both actuator types. Because of the advantageous actuator position, the stack actuator is better suited to control higher frequencies. Introduction Vibration control can be achieved by either passive or active methods. Passive vibration control approaches suffer from being ineffective at low frequencies. O..

Esa/Pagmo2: Pagmo 2.7

This release adds 3 new algorithms: particle swarm optimization generational (GPSO), exponential natural evolution strategies (xNES), improved harmony search (IHS). The full changelog is available, as usual, here: https://esa.github.io/pagmo2/changelog.htm

Symbolic Regression on Network Properties

Networks are continuously growing in complexity, which creates challenges for determining their most important characteristics. While analytical bounds are often too conservative, the computational effort of algorithmic approaches does not scale well with network size. This work uses Cartesian Genetic Programming for symbolic regression to evolve mathematical equations that relate network properties directly to the eigenvalues of network adjacency and Laplacian matrices. In particular, we show that these eigenvalues are powerful features to evolve approximate equations for the network diameter and the isoperimetric number, which are hard to compute algorithmically. Our experiments indicate a good performance of the evolved equations for several real-world networks and we demonstrate how the generalization power can be influenced by the selection of training networks and feature sets.Network Architectures and ServicesEmbedded and Networked System

Toxicity Detection in Multiplayer Online Games

Social interactions in multiplayer online games are an essential feature for a growing number of players world-wide. However, this interaction between the players might lead to the emergence of undesired and unintended behavior, particularly if the game is designed to be highly competitive. Communication channels might be abused to harass and verbally assault other players, which negates the very purpose of entertainment games by creating a toxic player-community. By using a novel natural language processing framework, we detect profanity in chat-logs of a popular Multiplayer Online Battle Arena (MOBA) game and develop a method to classify toxic remarks. We show how toxicity is non-trivially linked to game success.Network Architectures and ServicesDistributed System