Genome-Wide Transcriptional Response of Silurana (Xenopus) tropicalis to Infection with the Deadly Chytrid Fungus

Emerging infectious diseases are of great concern for both wildlife and humans. Several highly virulent fungal pathogens have recently been discovered in natural populations, highlighting the need for a better understanding of fungal-vertebrate host-pathogen interactions. Because most fungal pathogens are not fatal in the absence of other predisposing conditions, host-pathogen dynamics for deadly fungal pathogens are of particular interest. The chytrid fungus Batrachochytrium dendrobatidis (hereafter Bd) infects hundreds of species of frogs in the wild. It is found worldwide and is a significant contributor to the current global amphibian decline. However, the mechanism by which Bd causes death in amphibians, and the response of the host to Bd infection, remain largely unknown. Here we use whole-genome microarrays to monitor the transcriptional responses to Bd infection in the model frog species, Silurana (Xenopus) tropicalis, which is susceptible to chytridiomycosis. To elucidate the immune response to Bd and evaluate the physiological effects of chytridiomycosis, we measured gene expression changes in several tissues (liver, skin, spleen) following exposure to Bd. We detected a strong transcriptional response for genes involved in physiological processes that can help explain some clinical symptoms of chytridiomycosis at the organismal level. However, we detected surprisingly little evidence of an immune response to Bd exposure, suggesting that this susceptible species may not be mounting efficient innate and adaptive immune responses against Bd. The weak immune response may be partially explained by the thermal conditions of the experiment, which were optimal for Bd growth. However, many immune genes exhibited decreased expression in Bd-exposed frogs compared to control frogs, suggesting a more complex effect of Bd on the immune system than simple temperature-mediated immune suppression. This study generates important baseline data for ongoing efforts to understand differences in response to Bd between susceptible and resistant frog species and the effects of chytridiomycosis in natural populations

Multi-attribute vehicle routing problems: Exploring the limits of exact methods in practice

The companies CQM and EVO-it work together to help many different companies with solving their vehicle routing problems. EVO-it provides the interface of the software that the companies can use for route planning and CQM provides the technology that is used to solve the route planning problems, which concerns a lot of mathematical programming. The difficulty of solving these problems is that real-life problems usually deal withmulti-attribute vehicle routing problems, which are problemswith multiple characteristics, such as time windows and vehicle restrictions. These characteristics influence the solving process and the quality of the solution of the method used to solve the problem. Since these attributes can vary a lot per company, it is a difficult task for CQM to provide the best solving method for all problem types. Even though CQM has provided a very complex method that can handle many problems, it is unclear how far from the optimum solution the provided solutions are.The goal of this project was to investigate the improvement possibilities of the current method used by CQM.In this thesis many different solving methods for the vehicle routing problem are evaluated and their qualities are discussed. Furthermore, information from the companies that are using the software of EVO-it is collected. With this information an overview of the problem types occurring at these companies is provided. Such an overview had not been made before and will be very useful for CQMand EVO-it to make more problem type specific improvements to the solving method.Furthermore, this thesis describes two exact methods for solving vehicle routing problems similar to the real-life problems occurring at the clients of EVO-it. One of these methods uses a three-index flow formulation and the other one uses a set partitioning formulation and is based on the method described by Baldacci andMingozzi. It is shown that this latter method can be used to solve instances with up to 40 orders. In addition, both methods can deal with several complicated attributes, including capacity constraints, distance constraints, different type of costs and heterogeneous vehicles. Moreover, it can even deal with instances where not all orders can or need to be scheduled. In such a case, the orders that are not scheduled will get a penalty. To the best of my knowledge, no previous exact method was able to deal with such instances.Applied Mathematic

Multi-Agent Reinforcement Learning for power grid topology optimization

Recent challenges in operating power networks arise from increasing energy demands and unpredictable renewable sources like wind and solar. While reinforcement learning (RL) shows promise in managing these networks, through topological actions like bus and line switching, efficiently handling large action spaces as networks grow is crucial. This paper presents a hierarchical multi-agent reinforcement learning (MARL) framework tailored for these expansive action spaces, leveraging the power grid's inherent hierarchical nature. Experimental results indicate the MARL framework's competitive performance with single-agent RL methods. We also compare different RL algorithms for lower-level agents alongside different policies for higher-order agents

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

Background: Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs). Methods: This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. Results: A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83–7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97–2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14–1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25–1.30]). Conclusions: In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable