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The energy challenges of overcoming climate change and economic and global political crises require not only the increased integration of renewable energies but also an optimisation of energy generation and use and, as a result, a reduction in energy consumption in various sectors. Thermal energy consumption in buildings in particular accounts for a significant proportion of final energy consumption. With respect to commercial buildings, a central problem in optimising the system settings is the lack of or only limited information about the actual room temperatures as well as the comfort requirements and temperature perception of the users in the rooms on the one hand and the operational management and settings specified by the facility management, for example, the heating curves of the heating circuits, on the other. The aim here is to create a bidirectional flow of data and information so that the compromise between the necessary room temperatures and the comfort of the users and the most energy-efficient operational management possible can be achieved. In this context, the paper presents a developed and tested web platform that makes it possible to optimise the operation of the system technology, particularly the heating system in the building, from an energy point of view and to involve the building user (e.g., office employees) and to pass on information to the facility management, thus pursuing a holistic approach. In the associated Living Lab project (called ComfortLab), it was possible to obtain over 6500 votes on temperature perception and combine this with building operation and the parameters relevant to facility management. This made it possible to bridge the gap between user requirements and room temperatures on the one hand and energy consumption and the inlet temperature of the heating system and supply circuits on the other. The use of the platform makes it possible to optimise the setpoint specification, specifically the inlet temperature of individual heating circuits, considering both regular building operation at times of presence and the setting of weekend and night setback times. The results show a diversified picture regarding temperature perception and possible room temperature reductions of several degrees Celsius and energy savings in the double-digit percentage range.

Despite the importance of the role of Climate Finance to comply with the United Nations Framework Convention on Climate Change 1.5°C objective, there is no consensus on the definition of Climate Finance and the estimated assessment of its aggregated flows and effects remains challenging. Despite being a major emitter and having a significant and cost-effective mitigation potential, the livestock sector has so far only received a marginal share of Climate Finance. As demand for animal protein products continues to increase (68% between 2010 and 2050), there is a compelling case for channeling more Climate Finance investments into the sector to incentivize greenhouse gas emissions reduction at scale. Bottlenecks in linking the livestock sector to Climate Finance include the insufficient capacity to assess the cost-benefit of projects, high upfront cost and risk perception of investors, the informality of the sector, non-existence of Climate Finance instruments dedicated to the livestock sector and lack of cost-efficient Monitoring, Reporting and Verification systems. Nevertheless, recent developments provide avenues to increase the access of the animal protein sector to Climate Finance.

The emergence of resistance to targeted therapies restrains their efficacy. The development of rationally guided drug combinations could overcome this currently insurmountable clinical challenge. However, our limited understanding of the trajectories that drive the outgrowth of resistant clones in cancer cell populations precludes design of drug combinations to forestall resistance. Here, we propose an iterative treatment strategy coupled with genomic profiling and genome-wide CRISPR activation screening to systematically extract and define preexisting resistant subpopulations in an EGFR-driven lung cancer cell line. Integrating these modalities identifies several resistance mechanisms, including activation of YAP/TAZ signaling by WWTR1 amplification, and estimates the associated cellular fitness for mathematical population modeling. These observations led to the development of a combination therapy that eradicated resistant clones in large cancer cell line populations by exhausting the spectrum of genomic resistance mechanisms. However, a small fraction of cancer cells was able to enter a reversible nonproliferative state of drug tolerance. This subpopulation exhibited mesenchymal properties, NRF2 target gene expression, and sensitivity to ferroptotic cell death. Exploiting this induced collateral sensitivity by GPX4 inhibition clears drug-tolerant populations and leads to tumor cell eradication. Overall, this experimental in vitro data and theoretical modeling demonstrate why targeted mono- and dual therapies will likely fail in sufficiently large cancer cell populations to limit long-term efficacy. Our approach is not tied to a particular driver mechanism and can be used to systematically assess and ideally exhaust the resistance landscape for different cancer types to rationally design combination therapies., Significance: Unraveling the trajectories of preexisting resistant and drug-tolerant persister cells facilitates the rational design of multidrug combination or sequential therapies, presenting an approach to explore for treating EGFR-mutant lung cancer., (©2023 American Association for Cancer Research.)