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In the context of increasing electrification and the automation of future mobility, research and development of efficient powertrains requires enhanced test methods. One important aspect to consider is the complex interactions among a smart driving strategy, traffic flow, an individual vehicle's energy demand and emissions, and the corresponding overall values. Until now, the respective development domains of traffic flow control, powertrain control, component design, and intervehicle communication have usually been considered separately. This article presents a methodology that combines these areas and enables developers to obtain deep and highly realistic system insights, taking into account the mutual interactions among the domains. For this purpose, an X-in-the-loop validation platform is constructed that builds upon vehicular networking, road traffic, and a vehicle simulator, all coupled with two real drive-unit component test benches. We further show how this methodology can be used to investigate the effects of a novel predictive powertrain control algorithm that takes vehicle-to-everything (V2X) communication into account. Focusing on a typical urban reference route, we demonstrate that our algorithm enables performance to be maintained using electric motors with reduced specifications.

Urban mobility is rapidly shifting toward electrification. Yet, limited range and availability of charging infrastructure will remain issues for Electric Vehicles (EVs) for the foreseeable future. Effectively managing these limitations for both individual vehicles as well as entire regions and cities requires accurate predictions of energy demand. Current tool chains such as Simulation of Urban Mobility (SUMO) already make use of simplistic energy consumption models. However, these models are often applicable only to specific vehicles and do not consider individual powertrain components' characteristics. We close this gap and present a physics-based model for calculating energy consumption using component level modeling of the vehicle powertrain. The model is validated for EVs of different car segments against manufacturer data and test bench measurements. Through an open-source integration into SUMO, the model is available for public use. To showcase the possibilities of the proposed model and its flexibility, we perform a case study to predict the future energy demand of an exemplary mid-sized European city. The results show a strongly increasing demand for electric energy and underline the importance of considering heterogeneous vehicle fleets with different individual energy efficiency levels.

In this article, we analyze the interaction between powertrain technology, predictive driving functionalities, and inner-city traffic conditions. A model predictive velocity control algorithm is developed that utilizes dynamic traffic data as well as static route information to optimize the future trajectory of the considered ego-vehicle. This controller is then integrated into a state-of-the-art simulation environment for automated driving functionalities to calculate energy saving potentials for vehicles with a conventional gasoline engine powertrain and a P3-hybrid powertrain configuration as well as for a battery electric vehicle based on real driving measurements. The comparison of these powertrains under various traffic conditions shows that all three technologies profit from predictive driving functionalities. The determined reduction in energy demand ranges from 15% to more than 40%, but it is highly dependent on the boundary conditions and the selected powertrain technology. Specifically, it is shown that electrified powertrains can profit the most when the time-gap to the preceding vehicle is maintained at a high level. For a conventional powertrain, this effect is less pronounced and can be attributed to the efficiency characteristics of gasoline engines. It can be concluded that the development of advanced predictive driving functionalities requires microscopic simulation of inner-city traffic to achieve optimum results with regard to energy consumption.

Nuclear retention of incompletely spliced or mature mRNAs emerges as a novel, previously underappreciated layer of gene regulation, which enables the cell to rapidly respond to stress, viral infection, differentiation cues or changing environmental conditions. Focusing on mammalian cells, we discuss recent insights into the mechanisms and functions of nuclear retention, describe retention-promoting features in protein-coding transcripts and propose mechanisms for their regulated release into the cytoplasm. Moreover, we discuss examples of how aberrant nuclear retention of mRNAs may lead to human diseases.

An increasing level of driving automation and a successive electrification of modern powertrains enable a higher degree of freedom to improve vehicle fuel efficiency and reduce pollutant emissions. Currently, both domains themselves, driving automation as well as powertrain electrification, face the challenge of a rising development complexity with extensive use of virtual testing environments. However, state-of-the-art virtual testing environments typically strictly focus on just one domain and neglect the other. This paper shows the results of a simulation-based case study considering both domains simultaneously. The influence of energy saving automated functionalities on a conventional, a hybrid, and a pure electric powertrain is investigated for a carefully selected inner-city driving scenario. The vehicle simulation models for the different powertrain configurations are calibrated using test bench results and vehicle measurements. A model predictive acceleration controller is developed for realizing the speed optimization function. By considering traffic conditions such as traffic light schedules and a preceding vehicle as the boundary conditions, unnecessary accelerations and decelerations are avoided to reduce the energy demand. The case study is realized by applying this function to the three powertrains variants. As a final result, a clear difference in energy demand is observed: the hybrid powertrain benefits the most in terms of energy demand reduction in the given use case. The results clearly underscore that in future vehicle development programs, the powertrain and the real-world driving functionalities have to be optimized simultaneously to minimize the energy demand during everyday vehicle operation.

SR proteins connect nuclear pre-mRNA processing to mRNA export and translation. Botti et al. develop a quantitative nucleocytoplasmic shuttling assay and show that SR proteins are differentially modified and active in differentiated and pluripotent cells., SR proteins function in nuclear pre-mRNA processing, mRNA export, and translation. To investigate their cellular dynamics, we developed a quantitative assay, which detects differences in nucleocytoplasmic shuttling among seven canonical SR protein family members. As expected, SRSF2 and SRSF5 shuttle poorly in HeLa cells but surprisingly display considerable shuttling in pluripotent murine P19 cells. Combining individual-resolution cross-linking and immunoprecipitation (iCLIP) and mass spectrometry, we show that elevated arginine methylation of SRSF5 and lower phosphorylation levels of cobound SRSF2 enhance shuttling of SRSF5 in P19 cells by modulating protein–protein and protein–RNA interactions. Moreover, SRSF5 is bound to pluripotency-specific transcripts such as Lin28a and Pou5f1/Oct4 in the cytoplasm. SRSF5 depletion reduces and overexpression increases their cytoplasmic mRNA levels, suggesting that enhanced mRNA export by SRSF5 is required for the expression of pluripotency factors. Remarkably, neural differentiation of P19 cells leads to dramatically reduced SRSF5 shuttling. Our findings indicate that posttranslational modification of SR proteins underlies the regulation of their mRNA export activities and distinguishes pluripotent from differentiated cells.

Serine- and arginine-rich proteins (SR proteins) are a family of multitasking RNA-binding proteins (RBPs) that are key determinants of messenger ribonucleoprotein (mRNP) formation, identity and fate. Apart from their essential functions in pre-mRNA splicing, SR proteins display additional pre- and post-splicing activities and connect nuclear and cytoplasmic gene expression machineries. Through changes in their post-translational modifications (PTMs) and their subcellular localization, they provide functional specificity and adjustability to mRNPs. Transcriptome-wide UV crosslinking and immunoprecipitation (CLIP-Seq) studies revealed that individual SR proteins are present in distinct mRNPs and act in specific pairs to regulate different gene expression programmes. Adopting an mRNP-centric viewpoint, we discuss the roles of SR proteins in the assembly, maturation, quality control and turnover of mRNPs and describe the mechanisms by which they integrate external signals, coordinate their multiple tasks and couple subsequent mRNA processing steps.

Urban settings are challenging environments to implement eco-driving strategies for automated vehicles. It is often assumed that sufficient information on the preceding vehicle pulk is available to accurately predict the traffic situation. Because vehicle-to-vehicle communication was introduced only recently, this assumption will not be valid until a sufficiently high penetration of the vehicle fleet has been reached. Thus, in the present study, we employed Reinforcement Learning (RL) to develop eco-driving strategies for cases where little data on the traffic situation are available. An A-segment electric vehicle was simulated using detailed efficiency models to accurately determine its energy-saving potential. A probabilistic traffic environment featuring signalized urban roads and multiple preceding vehicles was integrated into the simulation model. Only information on the traffic light timing and minimal sensor data were provided to the control algorithm. A twin-delayed deep deterministic policy gradient (TD3) agent was implemented and trained to control the vehicle efficiently and safely in this environment. Energy savings of up to 19% compared with a simulated human driver and up to 11% compared with a fine-tuned Green Light Optimal Speed Advice (GLOSA) algorithm were determined in a probabilistic traffic scenario reflecting real-world conditions. Overall, the RL agents showed a better travel time and energy consumption trade-off than the GLOSA reference.

Reporter genes facilitate the characterization of promoter activities, transcript stabilities, translational efficiencies, or intracellular localization. Various reporter genes for Escherichia coli have been established, however, most of them have drawbacks like transcript instability or the inability to be used in genetic selections. Therefore, the glpD gene encoding glycerol-3-phosphate dehydrogenase was introduced as a novel reporter gene for E. coli. The enzymatic assay was optimized, and it was verified that growth on glycerol strictly depends on the presence of GlpD. The 5'-UTRs of three E. coli genes were chosen and cloned upstream of the new reporter gene glpD as well as the established reporter genes lacZ and gusA. Protein and transcript levels were quantified and translational efficiencies were calculated. The lacZ transcript was very unstable and its level highly depended on its translation, compromising its use as a reporter. The results obtained with gusA and glpD were similar, however, only glpD can be used for genetic selections. Therefore, glpD was found to be a superior novel reporter gene compared to the established reporter genes lacZ and gusA.

Serine- and arginine-rich proteins (SR proteins) are a family of multitasking RNA-binding proteins (RBPs) that are key determinants of messenger ribonucleoprotein (mRNP) formation, identity and fate. Apart from their essential functions in pre-mRNA splicing, SR proteins display additional pre- and post-splicing activities and connect nuclear and cytoplasmic gene expression machineries. Through changes in their post-translational modifications (PTMs) and their subcellular localization, they provide functional specificity and adjustability to mRNPs. Transcriptome-wide UV crosslinking and immunoprecipitation (CLIP-Seq) studies revealed that individual SR proteins are present in distinct mRNPs and act in specific pairs to regulate different gene expression programmes. Adopting an mRNP-centric viewpoint, we discuss the roles of SR proteins in the assembly, maturation, quality control and turnover of mRNPs and describe the mechanisms by which they integrate external signals, coordinate their multiple tasks and couple subsequent mRNA processing steps.

Translation initiation in 50–70 % of transcripts in Escherichia coli requires base pairing between the Shine–Dalgarno (SD) motif in the mRNA and the anti-SD motif at the 3′ end of the 16S rRNA. However, 30–50 % of E. coli transcripts are non-canonical and are not preceded by an SD motif. The 5′ ends of 44 E. coli transcripts were determined, all of which contained a 5′-UTR (no leaderless transcripts), but only a minority contained an SD motif. The 5′-UTR lengths were compared with those listed in RegulonDB and reported in previous publications, and the identities and differences were obtained in all possible combinations. We aimed to quantify the translational efficiencies of non-canonical 5′-UTRs using GusA reporter gene assays and Northern blot analyses. Ten non-canonical 5′-UTRs and two control 5′-UTRs with an SD motif were cloned upstream of the gusA gene. The translational efficiencies were quantified under five different conditions (different growth rates via two different temperatures and two different carbon sources, and heat shock). The translational efficiencies of the non-canonical 5′-UTRs varied widely, from 5 to 384 % of the positive control. In addition, the non-canonical transcripts did not exhibit a common regulatory pattern with changing environmental parameters. No correlation could be observed between the translational efficiencies of the non-canonical 5′-UTRs and their lengths, sequences, GC content, or predicted secondary structures. The introduction of an SD motif enhanced the translational efficiency of a poorly translated non-canonical transcript, while the efficiency of a well-translated non-canonical transcript remained unchanged. Taken together, the mechanisms of translation initiation at non-canonical transcripts in E. coli still need to be elucidated.