Occupancy maps of 208 chromatin-associated proteins in one human cell type

Transcription factors are DNA-binding proteins that have key roles in gene regulation. Genome-wide occupancy maps of transcriptional regulators are important for understanding gene regulation and its effects on diverse biological processes. However, only a minority of the more than 1,600 transcription factors encoded in the human genome has been assayed. Here we present, as part of the ENCODE (Encyclopedia of DNA Elements) project, data and analyses from chromatin immunoprecipitation followed by high-throughput sequencing (ChIP–seq) experiments using the human HepG2 cell line for 208 chromatin-associated proteins (CAPs). These comprise 171 transcription factors and 37 transcriptional cofactors and chromatin regulator proteins, and represent nearly one-quarter of CAPs expressed in HepG2 cells. The binding profiles of these CAPs form major groups associated predominantly with promoters or enhancers, or with both. We confirm and expand the current catalogue of DNA sequence motifs for transcription factors, and describe motifs that correspond to other transcription factors that are co-enriched with the primary ChIP target. For example, FOX family motifs are enriched in ChIP–seq peaks of 37 other CAPs. We show that motif content and occupancy patterns can distinguish between promoters and enhancers. This catalogue reveals high-occupancy target regions at which many CAPs associate, although each contains motifs for only a minority of the numerous associated transcription factors. These analyses provide a more complete overview of the gene regulatory networks that define this cell type, and demonstrate the usefulness of the large-scale production efforts of the ENCODE Consortium

Model of Human Vehicle Driving - a Predictive Nonlinear Optimization Approach

When driving a vehicle the human acts as a controller in a highly dynamic environment. Thus human behavior in that control loop has to a large extent been described using control theoretical methodology. We develop a driver model, in which driving is seen as a model predictive control task in such a way that the driver accumulates knowledge about his/her vehicle's handling properties. He/she builds a model out of that knowledge and uses it to predict the vehicle's future reactions on his/her control inputs. The human's behavioral optimization is reflected in the driver model by using that prediction model in order to optimize control inputs such, that a set of criteria, which reflect human well-being, are minimized. Prediction models and criteria depend on the current driving situation and on personal driver preferences. The principal properties of the driver model are discussed using very simple standard maneuvers like driving straight and cornering under different preferences. The method is then applied to a more complex track. The findings from that are backed up by experiments done in real world and in a driving simulator

Model of Human Vehicle Driving - a Predictive Nonlinear Optimization Approach

When driving a vehicle the human acts as a controller in a highly dynamic environment. Thus human behavior in that control loop has to a large extent been described using control theoretical methodology. We develop a driver model, in which driving is seen as a model predictive control task in such a way that the driver accumulates knowledge about his/her vehicle's handling properties. He/she builds a model out of that knowledge and uses it to predict the vehicle's future reactions on his/her control inputs. The human's behavioral optimization is reflected in the driver model by using that prediction model in order to optimize control inputs such, that a set of criteria, which reflect human well-being, are minimized. Prediction models and criteria depend on the current driving situation and on personal driver preferences. The principal properties of the driver model are discussed using very simple standard maneuvers like driving straight and cornering under different preferences. The method is then applied to a more complex track. The findings from that are backed up by experiments done in real world and in a driving simulator

Validating automated driving systems by using scenario-based testing: The Fuse4Rep process model for scenario generation as part of the 'Dresden Method

Scenario-based testing emerges as the main approach to validate automated driving systems (ADS) and thus ensure safe road traffic. Thereby, the test scenarios used should represent the traffic event of the corresponding operational design domain (ODD) and should cover the traffic situation from normal driving to an accident. For this, the fusion of police accident data and video-based traffic observation data into one database for subsequent scenario generation is advisable. Therefore, this paper presents the Fuse4Representativity (Fuse4Rep) process model as part of the Dresden Method, which helps to fuse heterogeneous data sets into one ODD-representative database for lean, fast and comprehensive scenario generation. Hereby, statistical matching is used as the fusion approach building on probable matching variables, such as the 3-digit accident type, the collision type and the misconduct of participants. Moreover, the paper shows how the scenarios generated in this way can be hypothetically used to validate ADS, e.g. in a stochastic traffic simulation incorporating human driver behaviour models. Future studies should apply the Fuse4Rep model in practice and test its validity

Der Güterverkehr von morgen

DER GÜTERVERKEHR VON MORGEN Der Güterverkehr von morgen / Prokop, Günther (CC BY-NC-ND) ( -

Validating automated driving systems by using scenario-based testing: The Fuse4Rep process model for scenario generation as part of the 'Dresden Method

Scenario-based testing emerges as the main approach to validate automated driving systems (ADS) and thus ensure safe road traffic. Thereby, the test scenarios used should represent the traffic event of the corresponding operational design domain (ODD) and should cover the traffic situation from normal driving to an accident. For this, the fusion of police accident data and video-based traffic observation data into one database for subsequent scenario generation is advisable. Therefore, this paper presents the Fuse4Representativity (Fuse4Rep) process model as part of the Dresden Method, which helps to fuse heterogeneous data sets into one ODD-representative database for lean, fast and comprehensive scenario generation. Hereby, statistical matching is used as the fusion approach building on probable matching variables, such as the 3-digit accident type, the collision type and the misconduct of participants. Moreover, the paper shows how the scenarios generated in this way can be hypothetically used to validate ADS, e.g. in a stochastic traffic simulation incorporating human driver behaviour models. Future studies should apply the Fuse4Rep model in practice and test its validity

Generating representative test scenarios: The FUSE for Representativity (fuse4rep) process model for collecting and analysing traffic observation data

Scenario-based testing is a pillar of assessing the effectiveness of automated driving systems (ADSs). For data-driven scenario-based testing, representative traffic scenarios need to describe real road traffic situations in compressed form and, as such, cover normal driving along with critical and accident situations originating from different data sources. Nevertheless, in the choice of data sources, a conflict often arises between sample quality and depth of information. Police accident data (PD) covering accident situations, for example, represent a full survey and thus have high sample quality but low depth of information. However, for local video-based traffic observation (VO) data using drones and covering normal driving and critical situations, the opposite is true. Only the fusion of both sources of data using statistical matching can yield a representative, meaningful database able to generate representative test scenarios. For successful fusion, which requires as many relevant, shared features in both data sources as possible, the following question arises: How can VO data be collected by drones and analysed to create the maximum number of relevant, shared features with PD? To answer that question, we used the Find–Unify–Synthesise–Evaluation (FUSE) for Representativity (FUSE4Rep) process model.We applied the first (“Find”) and second (“Unify”) step of this model to VO data and conducted drone-based VOs at two intersections in Dresden, Germany, to verify our results. We observed a three-way and a four-way intersection, both without traffic signals, for more than 27 h, following a fixed sample plan. To generate as many relevant information as possible, the drone pilots collected 122 variables for each observation (which we published in the ListDB Codebook) and the behavioural errors of road users, among other information. Next, we analysed the videos for traffic conflicts, which we classified according to the German accident type catalogue and matched with complementary information collected by the drone pilots. Last, we assessed the crash risk for the detected traffic conflicts using generalised extreme value (GEV) modelling. For example, accident type 211 was predicted as happening 1.3 times per year at the observed four-way intersection. The process ultimately facilitated the preparation of VO data for fusion with PD. The orientation towards traffic conflicts, the matched behavioural errors and the estimated GEV allowed creating accident-relevant scenarios. Thus, the model applied to VO data marks an important step towards realising a representative test scenario database and, in turn, safe ADSs

Reaktionsmuster der öffentlichen Verwaltung angesichts von Ereignissen mit (bisher) unbekannten Folgen

Manche neuartigen Folgen werdne uns erst bewußt, wenn sie "aufgetreten" sind, manchese Entstehen und einige Verlaufsformen solcher folgen können wir noch nicht hinreichen erklären oder nicht beherrschen. Solche besonderen Folgetypen haben wir als "schleichende Katastrophen" bezeichnet. Sie könnten vor allem für das politisch-administrative System schwer zu lösende Aufgaben produzieren. Wenn übliche Lösungsmuster nur begrenzt oder nicht unmittelbar greifen, müssen dann nicht ganz andere Denk- und Handlungsformen entwickelt werden? Drei jener schwierigen Problemfelder - in denen sich potentiell "schleichende Katastrophen" entwickeln könnten - wurden ausgesucht, um an ihnen die bisherigen Reaktionsmuster de spolitsich administrativen Systems mit Hilfe der Kategorien der Policy-Forschung zu ermitteln: AIDS, Neuartige Waldschäden und Umweltradioaktivität

Generating representative test scenarios: The FUSE for Representativity (fuse4rep) process model for collecting and analysing traffic observation data

Scenario-based testing is a pillar of assessing the effectiveness of automated driving systems (ADSs). For data-driven scenario-based testing, representative traffic scenarios need to describe real road traffic situations in compressed form and, as such, cover normal driving along with critical and accident situations originating from different data sources. Nevertheless, in the choice of data sources, a conflict often arises between sample quality and depth of information. Police accident data (PD) covering accident situations, for example, represent a full survey and thus have high sample quality but low depth of information. However, for local video-based traffic observation (VO) data using drones and covering normal driving and critical situations, the opposite is true. Only the fusion of both sources of data using statistical matching can yield a representative, meaningful database able to generate representative test scenarios. For successful fusion, which requires as many relevant, shared features in both data sources as possible, the following question arises: How can VO data be collected by drones and analysed to create the maximum number of relevant, shared features with PD? To answer that question, we used the Find–Unify–Synthesise–Evaluation (FUSE) for Representativity (FUSE4Rep) process model.We applied the first (“Find”) and second (“Unify”) step of this model to VO data and conducted drone-based VOs at two intersections in Dresden, Germany, to verify our results. We observed a three-way and a four-way intersection, both without traffic signals, for more than 27 h, following a fixed sample plan. To generate as many relevant information as possible, the drone pilots collected 122 variables for each observation (which we published in the ListDB Codebook) and the behavioural errors of road users, among other information. Next, we analysed the videos for traffic conflicts, which we classified according to the German accident type catalogue and matched with complementary information collected by the drone pilots. Last, we assessed the crash risk for the detected traffic conflicts using generalised extreme value (GEV) modelling. For example, accident type 211 was predicted as happening 1.3 times per year at the observed four-way intersection. The process ultimately facilitated the preparation of VO data for fusion with PD. The orientation towards traffic conflicts, the matched behavioural errors and the estimated GEV allowed creating accident-relevant scenarios. Thus, the model applied to VO data marks an important step towards realising a representative test scenario database and, in turn, safe ADSs