PI5P4Kα supports prostate cancer metabolism and exposes a survival vulnerability during androgen receptor inhibition.

Phosphatidylinositol (PI)regulating enzymes are frequently altered in cancer and have become a focus for drug development. Here, we explore the phosphatidylinositol-5-phosphate 4-kinases (PI5P4K), a family of lipid kinases that regulate pools of intracellular PI, and demonstrate that the PI5P4Kα isoform influences androgen receptor (AR) signaling, which supports prostate cancer (PCa) cell survival. The regulation of PI becomes increasingly important in the setting of metabolic stress adaptation of PCa during androgen deprivation (AD), as we show that AD influences PI abundance and enhances intracellular pools of PI-4,5-P2. We suggest that this PI5P4Kα-AR relationship is mitigated through mTORC1 dysregulation and show that PI5P4Kα colocalizes to the lysosome, the intracellular site of mTORC1 complex activation. Notably, this relationship becomes prominent in mouse prostate tissue following surgical castration. Finally, multiple PCa cell models demonstrate marked survival vulnerability following stable PI5P4Kα inhibition. These results nominate PI5P4Kα as a target to disrupt PCa metabolic adaptation to castrate resistance

Exploration of Systemic Strategies to Decarbonize Swiss Passenger Cars with a Focus on Vehicle Real-World Energy Demand

With the ratification of the Paris Agreement, Switzerland among many other countries, declared their ambition to limit global warming well below 2 °C, aiming for 1.5 °C. The increase in global temperature results from accumulated CO2 emissions in the atmosphere. There exist only a certain amount - a budget - of additional tolerable CO2 until we reach 1.5 °C. The governments need to quickly implement measures to reduce fossil CO2 emissions, also for passenger car transportation. The challenge to decarbonize passenger cars lies in the complexity of the system. Mobility is essential for a prosperous economy and cannot be eradicated. At the same time, different propulsion technologies exist, each with their advantages and limitations. Currently, there exists no single technology that unites all the benefits. Furthermore, the policy on vehicle emission limits relies on laboratory test values, but on-road emissions differ significantly. The assessment of the CO2 reduction of such a policy is not straight forward. This thesis presents a systematic approach to decarbonize passenger cars on the example of Switzerland. It spans the arc from the initial decarbonization intention to the exploration and quantification of different reduction measures. The ultimate results are possible strategies to achieve the set climate goals. The methodology is data-driven. The information for the modeling of propulsion technologies stems from an extensive measurement campaign designed to analyze real-world vehicle energy demand. Multiple cars of different technologies were monitored for two years on the road and simultaneously measured on a dynamometer test bench. The description of representative vehicle usage is based on national travel surveys and maintenance logs of a large car retailer. National vehicle register data builds the foundation to describe the fleet composition and substitution rate. The text underlines the relevance of new propulsion technologies, as the only measure to lead to zero CO2 emissions. However, we cannot rely on legislative vehicle emission values to asses effective emissions of conventional technologies and demanded electricity for pluggable cars, as they differ for on-road operation. The thesis presents an energy demand estimation tool to account for the discrepancy in on-road operation. The work further explores CO2 optimal technology solutions depending on the setup of the energy supply sector and daily travel distances. These analyses serve as bases to assess national reduction potentials, which account for technological feasibility to supply individual transportation demand. These explorations represent desirable, future solutions for strategic planning. In the last step, the thesis addresses the transformation timescales and strategies of the existing fleet. To remain within the 1.5 °C carbon budget, the thesis presents three paths. They all base on a penetration scenario of battery-electric cars and differ in the exploitation of non-battery electric technology and the deployment of renewable e-fuels. For Switzerland and the European Union, the electrification of passenger cars always results in a CO2 reduction. More than 80 % of the daily trips (but less concerning kilometer performance) can be covered by a small battery capacity vehicle like a plug-in hybrid. The additional electricity demand for the electrification of the entire fleet corresponds to about 35 % of today’s fuel energy content. Hydrogen is not needed for passenger cars and thus not considered in the decarbonization strategies. Current emission limits are not compatible with the 1.5 °C climate goal. To reach the target, the average emission of the new vehicles have to follow a linear decrease and reach zero by 2030. Unless we achieve a 100 % ramp-up in battery electric vehicles by 2030, synthetic, renewable e-fuels are crucial to staying within the CO2 budget. For a given ramp-up in battery electric vehicles, the necessary amount of e-fuels depends on the sold non-battery electric technology. The promotion of hybrid technologies, but also improvements in vehicle design bring the advantage of significantly reducing the peak demand of e-fuels and delaying their need for deployment. Thereby, they reduce the stress on infrastructure and fuel supply in the transformation period to a full-electric fleet and should be promoted

Datenlogger für die Kühlkette : Energy-Harvesting zum Aufladen des Akkus in einem RFID-Sensor-Label

Die Kühlkette in der Nahrungsmittel- und Pharmaindustrie braucht günstige und einfach bedienbare Klimadatenlogger. Um Temperatur und Luftfeuchtigkeit aufzuzeichnen, entwickelte ein Team der Zürcher Hochschule für Angewandte Wissenschaften ein biegsames Sensoretikett (Sensor-Label), das auf jede Verpackung passt. Als Stromquelle dient ein ausgeklügeltes Energy-Harvesting-System

Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data

The impending uptake of electric vehicles (EV) in worldwide car fleets is urging stakeholders to develop models that forecast impacts and risks of this transition. The most common modelling approaches rely on car movements provided in household travel surveys (HTS), despite their large data bias towards internal combustion engine vehicles. The scientific community has long wondered whether this characteristic of HTSs would undermine the conclusions drawn for EV mobility. This work applies state-of-the-art modelling techniques to the Swiss national HTS to conclusively prove, by means of validation, the reliability of these commonly used approaches. The cars tracked in the survey are converted to EVs, either pure battery or plug-in hybrids, and their performance is simulated over 4 consecutive days randomly sampled from the survey. EVs are allowed to charge at both residential and public locations at an adjustable charging power. Charging events are determined by a finely calibrated plugging-in decision scheme that depends on the battery’s state of charge. The resulting charging loads corroborate the validation, as these successfully compare with measurements obtained from several EV field tests. In addition, the study includes a sensitivity analysis that highlights the importance of accurately modelling various input parameters, especially EVs’ battery sizes and charging power. This work provides evidence that conventional HTSs are an appropriate instrument for generating EV insights, yet it adds guidelines to avoid modelling pitfalls and to maximise the simulation accuracy.ISSN:0306-2619ISSN:1872-911

Effects of Heavy-Duty Vehicle Electrification on Infrastructure: The Case of Switzerland

We present a method to simulate the charging (and battery swapping) energy demand of electrified trucks, and apply it to the example of Switzerland. We describe the daily mobility behavior of the Swiss fleet throughout a year, using governmental data sources. Based on this, we calculate the energy demand of each vehicle using vehicle and powertrain simulation. This then flows into a discrete event simulation, which we use to derive time-resolved charging power and battery swapping profiles. From that, we draw conclusions about the number of required swapping stations (respectively the average waiting time if there are not enough stations) and electrical loads they have to bear. We saw that, with better batteries and a maximum of three battery swaps per day, over 95% of heavy-duty vehicles can be electrified. This does not mean that every vehicle swaps its battery three times per day, and therefore the amount of extra batteries needed is not large. Nevertheless, to minimize the time loss for swapping, an adequate number and vehicle throughput of swapping stations should be guaranteed. For instance, to keep the waiting time under half an hour a day (duration of lunch break), a minimum of two swapping stations per large motorway fuel station and a throughput of at least eighteen vehicles per hour (per station) would be needed in Switzerland

Vehicle motion patterns for energy research: Comparison of annual mileage using vehicleand person-based data

Mobility patterns define the energy demand of passengers, respectively of the used vehicles. The daily and annual distribution of those patterns are of particular relevance to electric mobility; they define both the daily peak charging power demand and the overall need for additional electricity generation capacity. These are crucial inputs for life cycle assessments and energy research in general. A common approach to describe mobility patterns on a national level is to categorize a limited amount of personal mobility information (e.g. microcensus) and extrapolate it onto the whole population. The quality of the mobility behaviour distribution is highly dependent on the size and quality of the sample data. We present a comparison of two different approaches for describing the vehicle annual mileage. To that end, we analyse the distribution of mileage with data collected at different scales, i.e. daily resolved data from microcensus and yearly resolved data from maintenance logs. We conclude with a discussion on the potential complementarity of both approaches to gain further insights into individual mobility

Future mobility demand estimation based on sociodemographic information: A data-driven approach using machine learning algorithms

Estimations of the future mobility demand are highly valuable for policymakers, transportation planners, and the automotive industry. Knowing mobility patterns allows for targeted and optimized decarbonization of the transport sector. This work provides a model order reduction approach for clustering mobility demand according to characteristic population groups that share similar travel behavior. Using Swiss household travel survey data and machine learning algorithms, the methodology developed in this paper allows for extrapolating future mobility demand based on socio-demographic information