Automatic identification of variables in epidemiological datasets using logic regression

textabstractBackground: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies

Zuversichtliche Einladung zu dem Schauspiel, von der traurigen Hinrichtung des Crispi, Käysers Constantini Magni Aeltesten Sohns, Welches mit gnädigster Bewilligung Des ... Herrn Albert Anthons, Der Vier Grafen des Heil. Römischen Reichs, Grafens zu Schwartzburg und Hohnstein ... An statt des ordentlichen Herbst-Actus Auf unsern Schul Theatro zu Belustigung der studierenden Jugend, Den 24. 25. und 26. November allezeit Nachmittags um 2. Uhr soll fürgestellet werden unter der Direction

ZUVERSICHTLICHE EINLADUNG ZU DEM SCHAUSPIEL, VON DER TRAURIGEN HINRICHTUNG DES CRISPI, KÄYSERS CONSTANTINI MAGNI AELTESTEN SOHNS, WELCHES MIT GNÄDIGSTER BEWILLIGUNG DES ... HERRN ALBERT ANTHONS, DER VIER GRAFEN DES HEIL. RÖMISCHEN REICHS, GRAFENS ZU SCHWARTZBURG UND HOHNSTEIN ... AN STATT DES ORDENTLICHEN HERBST-ACTUS AUF UNSERN SCHUL THEATRO ZU BELUSTIGUNG DER STUDIERENDEN JUGEND, DEN 24. 25. UND 26. NOVEMBER ALLEZEIT NACHMITTAGS UM 2. UHR SOLL FÜRGESTELLET WERDEN UNTER DER DIRECTION Zuversichtliche Einladung zu dem Schauspiel, von der traurigen Hinrichtung des Crispi, Käysers Constantini Magni Aeltesten Sohns, Welches mit gnädigster Bewilligung Des ... Herrn Albert Anthons, Der Vier Grafen des Heil. Römischen Reichs, Grafens zu Schwartzburg und Hohnstein ... An statt des ordentlichen Herbst-Actus Auf unsern Schul Theatro zu Belustigung der studierenden Jugend, Den 24. 25. und 26. November allezeit Nachmittags um 2. Uhr soll fürgestellet werden unter der Direction ([1]) Title page ([1]) Text ([2]

Into the Unknown - Autonomous Navigation of the MMX Rover on the Unknown Surface of Mars' Moon Phobos

The MMX - Martian Moons eXploration - mission, as the name already suggests, aims to explore the two moons of Mars, Phobos and Deimos. The goal of this space mission led by JAXA is to acquire the scientific data necessary to understand the composition, structure, and history of these peculiar celestial bodies. The first man-made object to ever land on the larger and closer of these two moons, Phobos, shall be the small and lightweight MMX rover. The rover will be designed, manufactured and operated jointly by CNES and DLR. After separation from the carrier spacecraft, landing, uprighting, and deployment, this rover will start to drive in the low-gravity environment of Phobos surface and perform scientific operations. The rover will have a length of 44cm and a weight below 30kg. It will be solar-powered to operate for an intended mission duration of 90 days [1]. Communication round-trip times between Earth and Mars are already eight to forty minutes. For Phobos, we expect significantly higher values due to the need for relay satellites and limited communication windows. This leads to a requirement for a high level of autonomy for the robot, particularly its navigation capabilities, in order to maximize the scientific output of the mission. We will thus develop a navigation solution and integrate it as a software component running on the MMX rovers on-board computer. This navigation software will be verified in the introductory commissioning phase and is intended to be useful in the subsequent main operations phase. On the one hand, our design is inspired by the previous successful NASA planetary rovers, which have been the pioneers of this software technology. On the other hand, the design of the MMX rover brings its own sets of specifics and limitations to the table, and the uncharted celestial body Phobos itself is a source of several major and unprecedented challenges. For some of these, we can build upon the experience gained with the MASCOT mobile asteroid lander [2], which was deployed on the asteroid Ryugu in 2018 and successfully performed jumps for relocalization in the micro-gravity environment. The most notable challenges provided by the MMX rover design are the navigation cameras located at a fixed position and orientation w.r.t. the rovers body at a height of only 30cm above ground; the skid-steered locomotion of the rover limiting its turning speed; and restrictions on weight and power consumption further limiting the range of permissible operations. The most notable challenges provided by Phobos include not knowing the map of the terrain at the operational scale beforehand - the best maps available are at a resolution of 5m/px; the unknown soil composition; and the unknown local gravity. The combination of the rover and the celestial body is also a source of challenges: the behavior of wheels in contact with the soil is impossible to investigate beforehand, apart from software simulations, and Phobos very fast rotation - a Phobos day only lasts eight Earth hours - in the combination with the very slow maximum rover speed of approximately 4mm/s, makes shadows move relatively quickly, which could confuse visual odometry approaches. The on-board computer provides its own set of limitations and pre-requisites such as memory allocation and orchestration of concurrently running software processes. In our workshop contribution, we will identify and categorize challenges for navigation on Phobos and sketch our planned solutions to tackle them. Our planned navigation architecture will contain several FPGA and CPU-based modules: Dense depth data will be computed via Semi-Global Matching [3] on a FPGA. This is the basis for a stereo visual odometry such as [4] used to estimate the robot's trajectory. Further modules include an obstacle classification on individual depth images similar to [5] and possibly further mapping modules to create maps in compact representations to be sent to operators on Earth. Such obstacle and map information can then be used to realize autonomous emergency stop behavior up to future reactive obstacle avoidance or path planning modules to support (semi-)autonomous operation. These developments are based on experience we gained developing complex autonomous robotic navigation systems [6, 7] that we tested and evaluated in several field tests at Moon-analogue environments on the volcano Mt. Etna, Sicily, Italy [8, 9]. Some of the greatest challenges arise from the daring ambition to bring a rover into an environment where mankind has never been before, and expecting it to drive there to some extent autonomously. But that is also what makes this mission interesting in the first place. The scientific and technological discussions at the workshop may both help us to steer our decision making and enrich the scientific community with our findings. References: [1] J. Bertrand, et al., Roving on Phobos: Challenges of the MMX Rover for Space Robotics, ASTRA (2019) [2] J. Reill et al., MASCOT - Asteroid Lander with Innovative Mobility Mechanism, ASTRA (2015) [3] H. Hirschmüller, Stereo processing by semiglobal matching and mutual information, TPAMI (2007) [4] H. Hirschmüller, et al., Fast, unconstrained camera motion estimation from stereo without tracking and robust statistics, ICARCV (2002) [5] C. Brand, et al., Stereo-Vision Based Obstacle Mapping for Indoor / Outdoor SLAM, IROS (2014) [6] M. J. Schuster, et al., Towards Autonomous Planetary Exploration: The Lightweight Rover Unit (LRU), its Success in the SpaceBotCamp Challenge, and Beyond, JINT (2017) [7] M. J. Schuster, et al., Distributed stereo vision-based 6D localization and mapping for multi-robot teams, JFR (2018) [8] M. Vayugundla, et al., Datasets of Long Range Navigation Experiments in a Moon Analogue Environment on Mount Etna, ISR (2018) [9] A. Wedler, et al., First Results of the ROBEX Analogue Mission Campaign: Robotic Deployment of Seismic Networks for Future Lunar Missions, IAC (2017

Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics

Cancer stem cells drive disease progression and relapse in many types of cancer. Despite this, a thorough characterization of these cells remains elusive and with it the ability to eradicate cancer at its source. In acute myeloid leukemia (AML), leukemic stem cells (LSCs) underlie mortality but are difficult to isolate due to their low abundance and high similarity to healthy hematopoietic stem cells (HSCs). Here, we demonstrate that LSCs, HSCs, and pre-leukemic stem cells can be identified and molecularly profiled by combining single-cell transcriptomics with lineage tracing using both nuclear and mitochondrial somatic variants. While mutational status discriminates between healthy and cancerous cells, gene expression distinguishes stem cells and progenitor cell populations. Our approach enables the identification of LSC-specific gene expression programs and the characterization of differentiation blocks induced by leukemic mutations. Taken together, we demonstrate the power of single-cell multi-omic approaches in characterizing cancer stem cells.This project was financially supported by the Deutsche José Carreras Leukämie Stiftung grant DJCLS 20R/2017 (to L.V., S.H., L.M.S., and A.T.), the Emerson foundation grant 643577 (to L.V. and L.M.S.) and the German Bundesministerium für Bildung und Forschung (BMBF) through the Juniorverbund in der Systemmedizin “LeukoSyStem” (FKZ 01ZX1911D to L.V., S.H., and S.R). Contributions by S.R. were further supported by Emmy Noether Fellowship RA 3166/1-1 (DFG). Contributions by C.P. were supported by a Max-Eder Grant (German Cancer Aid 70111531). Contributions by D.N., J.C.J., W.K.H., and T.B. were supported by the Gutermuth Foundation, the H.W. & J. Hector fund, Baden-Württemberg, and the Dr. Rolf M. Schwiete Fund, Mannheim. D.N. is an endowed professor of the Deutsche José Carreras Leukämie Stiftung (DJCLS H 03/01

Automatic identification of variables in epidemiological datasets using logic regression

Background: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies