Event Attribution Research Articles

Formally combining different lines of evidence in extreme-event attribution

Abstract. Event attribution methods are increasingly routinely used to assess the role of climate change in individual weather events. In order to draw robust conclusions about whether changes observed in the real world can be attributed to anthropogenic climate change, it is necessary to analyse trends in observations alongside those in climate models, where the factors driving changes in weather patterns are known. Here we present a quantitative statistical synthesis method, developed over 8 years of conducting rapid probabilistic event attribution studies, to combine quantitative attribution results from multi-model ensembles and other, qualitative, lines of evidence in a single framework to draw quantitative conclusions about the overarching role of human-induced climate change in individual weather events.

English and Regional Media Coverage of the 2022 Heatwave in India

ABSTRACT The 2022 heatwave in India formed part of a pattern of extreme weather events in the country, which are becoming more intense, frequent, or prolonged. The aim of this study is to explore media coverage of climate change and extreme events in a multilingual media context such as India. Data analysis of a large and diverse sample of Indian media coverage of the 2022 heatwave (n = 309) suggests that only 1 in every 7 news articles (14%) mentioned climate change in their heatwave coverage in the English press, and less than 10% in the Hindi, Telugu, and Marathi language news media. However, many English-language articles reported the links between climate change and the heatwave, although the figure was much lower for the other languages. Two Event Attribution studies analyzing the heatwave were widely quoted, albeit with some inaccuracies. Indian scientists were the most cited, whereas politicians and NGOs were largely absent, in contrast to previous research. Journalists regularly covered three aspects that affect the impact of the heatwave on ordinary people, namely emergency responses, disaster planning, and vulnerabilities. This study concludes by exploring theoretical and practical recommendations for media coverage of heatwaves and climate change.

Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia

Abstract Computing the return times of extreme events and assessing the impact of climate change on such return times is fundamental to extreme event attribution studies. However, the rarity of such events in the observational record makes this task a challenging one, even more so for ‘record-shattering’ events that have not been previously observed at all. While climate models could be used to simulate such extremely rare events, such an approach entails a huge computational cost: gathering robust statistics for events with return time of centuries would require a few thousand years of simulation. In this study, we use an innovative tool, rare event algorithm, that allows us to sample numerous extremely rare events at a much lower cost than direct simulations. We employ the algorithm to sample extreme heatwave seasons, corresponding to large anomalies of the seasonal average temperature, in a heatwave hotspot of South Asia using the global climate model Plasim. We show that the algorithm estimates the return levels of extremely rare events with much greater precision than traditional statistical fits. It also enables the computation of various composite statistics, whose accuracy is demonstrated through comparison with a very long control run. In particular, our results reveal that extreme heatwave seasons are associated with an anticyclonic anomaly embedded within a large-scale hemispheric quasi-stationary wave-pattern. Additionally, the algorithm accurately represents the intensity-duration-frequency statistics of sub-seasonal heatwaves, offering insights into both seasonal and sub-seasonal aspects of extreme heatwave seasons. This innovative approach could be used in extreme event attribution studies to better constrain the changes in an event’s probability and intensity with global warming, particularly for events with return times spanning centuries or millennia.

Statistical properties of items and summary scores from the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE®) in a diverse cancer sample.

The Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE®) was developed to capture symptomatic adverse events from the patient perspective. We aim to describe statistical properties of PRO-CTCAE items and summary scores and to provide evidence for recommendations regarding PRO-CTCAE administration and reporting. Using data from the PRO-CTCAE validation study (NCT02158637), prevalence, means, and standard deviations of PRO-CTCAE items, composite scores, and mean and maximum scores across attributes (frequency, severity, and/or interference) of symptomatic adverse events were calculated. For each adverse event, correlations and agreement between attributes, correlations between attributes and composite scores, and correlations between composite, mean, and maximum scores were estimated. PRO-CTCAE items were completed by 899 patients with various cancer types. Most patients reported experiencing one or more adverse events, with the most prevalent being fatigue (87.7%), sad/unhappy feelings (66.0%), anxiety (63.6%), pain (63.2%), insomnia (61.8%), and dry mouth (60.0%). Attributes were moderately to strongly correlated within an adverse event (r = 0.53 to 0.77, all p < 0.001) but not fully concordant (κweighted = 0.26 to 0.60, all p < 0.001), with interference demonstrating lowest mean scores and prevalence among attributes of the same adverse event. Attributes were moderately to strongly correlated with composite scores (r = 0.67 to 0.97, all p < 0.001). Composite scores were moderately to strongly correlated with mean and maximum scores for the same adverse event (r = 0.69 to 0.94, all p < 0.001). Correlations between composite scores of different adverse events varied widely (r = 0.04 to 0.68) but were moderate to strong for conceptually related adverse events. Results provide evidence for PRO-CTCAE administration and reporting recommendations that the full complement of attributes be administered for each adverse event, and that attributes as well as summary scores be reported.

Strong Linkage Between Observed Daily Precipitation Extremes and Anthropogenic Emissions Across the Contiguous United States

AbstractThe results of probabilistic event attribution studies depend on the choice of the extreme value statistics used in the analysis, particularly with the arbitrariness in the selection of appropriate thresholds to define extremes. We bypass this issue by using the Extended Generalized Pareto Distribution (ExtGPD), which jointly models low precipitation with a generalized Pareto distribution and extremes with a different Pareto tail, to conduct daily precipitation attribution across the contiguous United States (CONUS). We apply the ExtGPD to 12 general circulation models from the Coupled Model Intercomparison Project Phase 6 and compare counterfactual scenarios with and without anthropogenic emissions. Observed precipitation by the Climate Prediction Center is used for evaluating the GCMs. We find that greenhouse gases rather than natural variability can explain the observed magnitude of extreme daily precipitation, especially in the temperate regions. Our results highlight an unambiguous linkage of anthropogenic emissions to daily precipitation extremes across CONUS.

Change pattern relationships in event logs

Process mining utilises process execution data to discover and analyse business processes. Event logs represent process executions, providing information about the activities executed. In addition to generic event attributes like activity name and timestamp, events might contain domain-specific attributes, such as a blood sugar measurement in a healthcare environment. Many of these values change during a typical process quite frequently. We refer to those as dynamic event attributes. Change patterns can be derived from dynamic event attributes, describing if the attribute values change from one activity to another. So far, change patterns can only be identified in an isolated manner, neglecting the chance of finding co-occuring change patterns. This paper provides an approach to identifying relationships between change patterns by utilising correlation methods from statistics. We applied the proposed technique on two event logs derived from the MIMIC-IV real-world dataset on hospitalisations in the US and evaluated the results with a medical expert. It turns out that relationships between change patterns can be detected within the same directly or eventually follows relation and even beyond that. Further, we identify unexpected relationships that are occurring only at certain parts of the process. Thus, the process perspective reveals novel insights on how dynamic event attributes change together during process execution. The approach is implemented in Python using the PM4Py framework.

Frontiers in attributing climate extremes and associated impacts

The field of extreme event attribution (EEA) has rapidly developed over the last two decades. Various methods have been developed and implemented, physical modelling capabilities have generally improved, the field of impact attribution has emerged, and assessments serve as a popular communication tool for conveying how climate change is influencing weather and climate events in the lived experience. However, a number of non-trivial challenges still remain that must be addressed by the community to secure further advancement of the field whilst ensuring scientific rigour and the appropriate use of attribution findings by stakeholders and associated applications. As part of a concept series commissioned by the World Climate Research Programme, this article discusses contemporary developments and challenges over six key domains relevant to EEA, and provides recommendations of where focus in the EEA field should be concentrated over the coming decade. These six domains are: (1) observations in the context of EEA; (2) extreme event definitions; (3) statistical methods; (4) physical modelling methods; (5) impact attribution; and (6) communication. Broadly, recommendations call for increased EEA assessments and capacity building, particularly for more vulnerable regions; contemporary guidelines for assessing the suitability of physical climate models; establishing best-practice methodologies for EEA on compound and record-shattering extremes; co-ordinated interdisciplinary engagement to develop scaffolding for impact attribution assessments and their suitability for use in broader applications; and increased and ongoing investment in EEA communication. To address these recommendations requires significant developments in multiple fields that either underpin (e.g., observations and monitoring; climate modelling) or are closely related to (e.g., compound and record-shattering events; climate impacts) EEA, as well as working consistently with experts outside of attribution and climate science more generally. However, if approached with investment, dedication, and coordination, tackling these challenges over the next decade will ensure robust EEA analysis, with tangible benefits to the broader global community.

Attribution of environmental outcomes to climate change: Perceptions of coastal communities in the Caribbean

Attribution of environmental outcomes to climate change is a live debate in scholarly and policy circles. Non-scientific attribution by the lay public has value for enriching the discourse, engagements, and decisions on loss and damage, adaptation and mitigation, communication strategies, and the science-policy interface from local to international levels. There is a need for context-specific studies on perceived climate attribution by the lay public. Unlike the few previous studies that focused on attribution of extreme events to climate change by the lay public, the current paper assessed the key compositional and contextual factors that influence the attribution of environmental outcomes with slow onset to climate change. This study surveyed 441 households in eight small coastal communities in three Caribbean countries and applied a generalized linear regression model with a complementary log-log link to assess the relationship between compositional and contextual factors that influence perceived linkage between climate change and environmental outcome. At the bivariate level, the results showed that ethnicity, marital status, household head highest education, household income, and community of living were significant predictors of the likelihood of the perceived linkage between climate change and worsening environmental outcome. At the multivariate level, when all factors have been accounted for, we found that respondents with higher education were 160% more likely to indicate that climate change has adverse effects on environmental outcomes. Respondents with 10 or more years of experience in their present occupation were 73% more likely to suggest a linkage between climate change and worse environmental outcomes. Other strong predictors included income, ethnicity and community of residence. The perceived relationship between climate change and environmental outcomes differed systematically based on the compositional and contextual attributes of respondents. Several significant relationships at the bivariate level persisted at the multivariate level, indicating that the perceived linkages are robust and entrenched. It is concluded that there are heterogeneities in the perceived relationship between climate change and environmental outcomes based on the compositional and contextual factors of the households. Higher education and longer experience in the same occupation contribute significantly to climate attribution by the lay public.

The Attribution of February Extremes over North America: A Forecast-Based Storyline Study

Abstract The importance of extreme event attribution rises as climate change causes severe damage to populations resulting from unprecedented events. In February 2019, a planetary wave shifted along the U.S.–Canadian border, simultaneously leading to troughing with anomalous cold events and ridging over Alaska and northern Canada with abnormal warm events. Also, a dry-stabilized anticyclonic circulation over low latitudes induced warm extreme events over Mexico and Florida. Most attribution studies compare the climate model simulations under natural or actual forcing conditions and assess probabilistically from a climatological point of view. However, in this study, we use multiple ensembles from an operational forecast model, promising statistical as well as dynamically constrained attribution assessment, often referred to as the storyline approach to extreme event attribution. In the globally averaged results, increasing CO2 concentrations lead to distinct warming signals at the surface, resulting mainly from diabatic heating. Our study finds that CO2-induced warming eventually affects the possibility of extreme events in North America, quantifying the impact of anthropogenic forcing over less than a week’s forecast simulation. Our study assesses the validity of the storyline approach conditional on the forecast lead times, which is hindered by rising noise in CO2 signals and the declining performance of the forecast model. The forecast-based storyline approach is valid for at least half of the land area within a 6-day lead time before the target extreme occurrence. Our attribution results highlight the importance of achieving net-zero emissions ahead of schedule to reduce the occurrence of severe heatwaves.

Current Tasks in Identifying Invalid Events in Critical Information Infrastructure

The purpose of the study is to develop an approach for identifying and processing invalid events in critical information infrastructure (CII) based on the concepts of taxonomy and categorization. The approach aims to improve the efficiency of identifying, classifying, and managing information security (IS) incidents. The article addresses the current tasks of ensuring the required level of CII protection and minimizing the negative consequences of information security incidents resulting from invalid events. The identification of these events is associated with the complexity of detecting such events, the need to process large volumes of data, insufficient speed in detecting IS events, as well as technological limitations.The relevance of identifying and classifying invalid events in information security, especially for CII, is driven by the need for timely detection and response to incidents that could lead to negative consequences. Understanding the nature and characteristics of such events allows for effective system protection and prevention of significant damage. To enhance the effectiveness of ensuring security, it is necessary to identify the class of invalid events among the numerous information security events by considering the characteristics that define invalid events.The novelty of the proposed approach lies in solving the task of identifying the class of invalid information security events based on taxonomy methods, involving the use of event categorization tools with the attributes of invalid events.Materials and methods. The approach to identifying invalid events in CII, based on the principles of information security event taxonomy, was used to solve the task. It was shown that identifying invalid information security events is directly related to solving the problem of searching for and analyzing their attributes, which represent the characteristics or parameters used to describe and classify security incidents. Based on the key principles of taxonomy, a model of the structure of the set of invalid events was developed to determine the characteristics that can be the basis for classifying invalid events. The process of identifying invalid information security events includes a sequence of stages: taxonomy, categorization, and classification, with appropriate methods and tools implemented at each stage.Results. Approaches to identifying invalid events in CII have been analyzed. Problems related to large data volumes, the complexity of event processing, the considerable time required for their detection, and technological limitations were considered. It was shown that the concept of taxonomy and categorization allows for effective identification and classification of information security incidents, ensuring efficient processing and response. The feasibility of applying taxonomy for describing and identifying the attributes of invalid events was justified, contributing to the development of effective protection strategies and improving security levels. A generalized scheme for processing invalid events was proposed, including a set of interconnected stages of identification, categorization, impact assessment, response, documentation, and analysis. An algorithm for structured description and classification of incidents was developed, allowing for more accurate and timely responses to information security threats.Conclusion. The results obtained increase the effectiveness of solving the task of classifying information security incidents by identifying invalid events, which reduces the level of negative consequences of incidents and enhances the security of CII objects.

Heat extremes linearly shift with global warming, with frequency doubling per decade since 1979

Abstract Heat extremes have been increasing both in frequency and in intensity in most land regions of the world, and this increase has been attributed to human activities. In the last decade, many outstanding and record shattering heat extremes have occurred worldwide, triggering fears of a nonlinear behaviour or an “acceleration” in the development of heat conditions, considering the warming level when the event occurred. Here we show that the evolution of yearly temperature maxima, with return periods above 10 years, consistently shifts with global temperatures and does not significantly depart from this behaviour in recent years or decades when considered globally or at the scale of continents. This result is obtained by using a classical statistical event attribution technique, where the assumption that the distribution of block-maxima extremes linearly shifts with global warming is tested across years and world land regions. However, the pace of frequency change is large, with the probability of heat extremes exponentially rising and nearly doubling every decade since 1979, particularly when considering events with a return period of about 10 to 50 years in 2000. This makes the climate of a decade ago unrepresentative of today’s climate. Our results overall mean that we do not expect events like the recent outstanding extremes to undergo nonlinear changes, despite fast changes. They also show that assumptions underlying attribution techniques used in many recent studies are consistent with recent temperature trends.&amp;#xD;

How can event attribution science underpin financial decisions on Loss and Damage?

With climate extremes hitting nations across the globe, disproportionately burdening vulnerable developing countries, the prompt operation of the Loss and Damage fund is of paramount importance. As decisions on resource disbursement at the international level, and investment strategies at the national level, loom, the climate science community's role in providing fair and effective evidence is crucial. Attribution science can provide useful information for decision makers, but both ethical implications and deep uncertainty cannot be ignored. Considering these aspects, we articulate a vision that integrates established attribution methods and multiple lines of evidence within a coherent logical framework.

Optimizing Database Performance in Complex Event Processing through Indexing Strategies

Complex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data continue to grow, optimizing the performance of CEP systems becomes a critical challenge. This paper investigates the impact of indexing strategies on the performance of databases handling complex event processing. We propose a novel indexing technique, called Hierarchical Temporal Indexing (HTI), specifically designed for the efficient processing of complex event queries. HTI leverages the temporal nature of event data and employs a multi-level indexing approach to optimize query execution. By combining temporal indexing with spatial- and attribute-based indexing, HTI aims to accelerate the retrieval and processing of relevant events, thereby improving overall query performance. In this study, we evaluate the effectiveness of HTI by implementing complex event queries on various CEP systems with different indexing strategies. We conduct a comprehensive performance analysis, measuring the query execution times and resource utilization (CPU, memory, etc.), and analyzing the execution plans and query optimization techniques employed by each system. Our experimental results demonstrate that the proposed HTI indexing strategy outperforms traditional indexing approaches, particularly for complex event queries involving temporal constraints and multi-dimensional event attributes. We provide insights into the strengths and weaknesses of each indexing strategy, identifying the factors that influence performance, such as data volume, query complexity, and event characteristics. Furthermore, we discuss the implications of our findings for the design and optimization of CEP systems, offering recommendations for indexing strategy selection based on the specific requirements and workload characteristics. Finally, we outline the potential limitations of our study and suggest future research directions in this domain.

Processes and principles for producing credible climate change attribution messages: lessons from Australia and New Zealand

Extreme event attribution (EEA) information is increasingly in demand from climate services. EEA messages can: raise awareness about the effect climate change has already imposed, inform climate change liability conversations, and be combined with climate projections to inform adaptation. However, due to limitations in observations, models and methods, there are barriers towards operationalising EEA in practice. Operational services will need EEA to be done transparently and using preset formats. Here we review recent experience and practice in EEA in Australia and New Zealand with a view to inform the design of an EEA component of climate services. We present a flow chart of the processes involved, noting particular care is needed on the trigger, event definition, and climate model evaluation, with effective stage gates. We also promote the use of tailored causal network diagrams as a standard tool to inform an EEA study and communicate results, with particular care needed for messages on events with lower confidence or complex sets of influences, including tropical cyclones and extratropical cyclones. We suggest that extending EEA to impact attribution is essential for making EEA messages salient but requires an uplift in forming interdisciplinary teams and in granular exposure and vulnerability datasets and is likely to raise new interdisciplinary methodological questions. Finally, we suggest communication of EEA messages can learn more from its origins in medical epidemiology.

Residents' perceived social sustainability of food tourism events

Amid the need for inclusive growth and well-being in sustainable tourism, residents' social sustainability needs must be understood. Based on self-perception and social exchange theories, this study examines residents' perceptions of the social sustainability of food festival events. Focusing on an East-West culinary fusion festival in Macau, the Macau Food Festival, 460 residents are surveyed to ascertain their perceptions of the social sustainability attributes, social sustainability impacts, and quality of life outcomes. Results of six direct and two moderation relationships revealed residents' perceptions of social sustainability attributes of the food festival event are a major predictor of the perceived impacts on the community and resident quality of life. A higher perceived quality of life was found to positively impact place attachment, support for tourism, and prosocial behaviors. The degree to which social sustainability attributes of the food festival impact residents' quality of life, though different, is positive for attendees and non-attendees implying that positive perceptions could be inferred through both introspection and extrospection. It, further, suggests the need for destination management organizations to understand residents' social sustainability needs of the food tourism events to introduce attributes that augment residents' quality of life and positive responses to tourism development.

Extraordinary 2021 snowstorm in Spain reveals critical threshold response to anthropogenic climate change

Attribution of extreme weather events to anthropogenic climate change (ACC) has become an increasingly important line of research in recent years. However, the potential influence of ACC on heavy snowstorms remains largely unexplored. Here we focus on studying the exceptional January 2021 snowfall event in Spain, known as Filomena. First, using observational data and flow analogs, we show that the characteristic synoptic pattern leading to the episode has not significantly changed in frequency over the past decades. Based on this, we assume a fixed dynamical pattern and focus on studying the influence of ACC on the thermodynamics of the event using an atmospheric model and a storyline attribution approach. Our simulations indicate that in northern highlands, ACC intensified snowfall by up to +40% compared with pre-industrial conditions, while in nearby southern lowlands ACC weakened snowfall by up to –80%. This characteristic shift from weakening to intensification is well defined by a critical threshold in temperature. Furthermore, we show that if Filomena were to occur at the end of the 21st century, this contrasting response to ACC would be enhanced. Altogether, our findings highlight the large but uneven impact of global warming on extreme snowstorm events.

Supernatural Attributions for Extraordinary Events: Examining Cognitive and Contextual Predictors

AbstractWhy do people make supernatural attributions (SA) for events? This article focused on five possible predictors: stressfulness, unusual events, religiousness, seeing a supernatural explanation as meaningful, and seeing supernatural entities as powerful. We also predicted that people would be more likely to adopt supernatural explanations seen as highly accessible, motivating, and plausible. We focused primarily on SA in general and secondarily on specific entities. We tested preregistered hypotheses using survey data in two samples of undergraduates: one reporting life‐changing events (N = 594) and another reporting on an event with a supernatural air, or supernaturally perceived events (N = 475). Results supported all proposed predictors except for stressfulness. Path analyses revealed that, in both samples, predictors were linked with seeing supernatural explanations as accessible, plausible, and motivating; these ratings, in turn, had positive associations with SA. These results begin to integrate previous theory and research on individual predictors of SA.

NCI MATCH (EAY131) arm Z1D: Retrospective evaluation of attributions of adverse events experienced on nivolumab in patients with MSI-high tumors.

e14711 Background: Machine learning algorithms for prediction of Adverse Events (AEs) due to immune checkpoint inhibitors (ICIs) rely on clinically annotated data to serve as gold standards. Clinical trials are the best sources of such data, but audits to assess validity are rare, compromising future machine learning efforts. We reviewed AEs reported on the National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH) EAY-131 Arm Z1D. Patients matched to Arm Z1D due to MSI-high status were treated with nivolumab. Routine and serious AEs were reported to NCI. Attribution data from AEs on 29 patients with tumor genomics suitable for machine learning approaches were obtained and reviewed retrospectively. Methods: Documented AEs were sorted by grade via the Common Terminology for Adverse Events (CTCAE) system and categorized as related or unrelated to ICIs. Primary source data reported to NCI were reviewed. If any ambiguity was found, trial sites were queried to provide additional information and documentation. Decisions were made by an experienced NCI medical officer with the benefit of additional data and site responses. All serious AEs and a subset of ambiguous routine AEs were assessed to determine if attribution conformed to expected attribution per the nivolumab package insert. Uncertain attribution required additional queries, including email or phone correspondence. Grade 1 or 2 events were only evaluated to determine if any attribution to ICIs was possible. If a higher grade event was attributed to ICIs, there was no further query for lower grade events on the same patient. Results: A total of 639 Grade 1, 152 Grade 2, 75 Grade 3, 10 Grade 4, and 5 Grade 5 events were reported among the 29 patients. One Grade 5 event, 5 Grade 4 events, 13 Grade 3 Events, 9 Grade 2 events, and 6 Grade 1 events were considered less likely to be attributed to ICIs compared with the initial sponsor or site investigator assessment of possibly, probably, or definitely related. Two Grade 4 events, 1 Grade 2 event, and 8 Grade 1 events were considered to be possibly, probably, or definitely due to ICIs compared with the initial sponsor or site assessment of less likely or unrelated. Site responsiveness to retrospective queries from the sponsor varied. Additional lab values, primary source documentation, and communication with site investigators were necessary to make modifications. A total of 11 AEs required additional queries to site investigators. Conclusions: Retrospective reviews of real-time site and sponsor assessments of attribution exhibit significant modification of initial attribution to ICIs in this review of 29 patients on Arm Z1D of the NCI MATCH trial. Machine learning approaches for prediction of AEs on patients treated with ICIs may require careful assessments of primary source data to develop adequate training sets, given the paucity of gold standard data available.

A phase I/II study of intraperitoneal (IP) paclitaxel (PTX) in patients with metastatic appendiceal adenocarcinoma.

TPS4215 Background: Appendiceal cancer is a rare, orphan disease, that is unfortunately becoming more common. There is growing data from our lab and others that particular subtypes of AA do not respond to CRC chemotherapy. The use of CRC chemotherapy to treat patients with AA tragically results in many patients enduring side effects from chemotherapy that is ineffective; however, this practice will likely continue unless AA-specific treatments are developed. The Shen lab has recently tested IP paclitaxel (PTX) in orthotopic PDX models of AA. After allowing implanted tumors to grow for four weeks to simulate diffuse peritoneal metastases, baseline MRI was performed and mice were randomly assigned to treatment groups of four mice and intraperitoneally (IP) administered increasing doses of PTX. Marked response was seen in all three models at dose of 25 mg/kg IP weekly, with a near complete response in model AAPDX-10. This dose given IP was well tolerated by the mice, notably this dose exceeds the know lethal IV PTX dose. Methods: The Phase I component of this study will be a prospective single-arm intervention trial giving IP PTX every 2 weeks for a total of 5 doses with primary endpoint of safety and tolerability (MTD determination). Each patient will have an initial diagnostic laparoscopy where PCI score will be calculated and IP catheter placed, dx lap is repeated at the end of study after 5 biweekly doses. For the Phase 1 portion of the trial a Bayesian Optimal Interval (BOIN) approach will be used to determine recommended phase 2 dose (RP2D). All patients treated at that dose or higher will be included in Phase 2 component of the study where the primary objective is response, determined by change in PCI score. With 15 patients Based on historical data, we estimate that the proportion of patients who will demonstrate an objective response without going on trial is 0.05, i.e., the null hypothesis is a 0.05 ORR. Treatment is expected to raise this proportion to 30%. Using an exact binomial test with one-sided type I error of 0.05, we will reject the null if at least 3 patients achieve response. This test has power of 87%. Secondary end points include pathologic, biochemical and radiographic response, PFS, OS, and the influence of KRAS/GNAS/TP53 mutation on response. PK measurements will also be performed during the Phase 1 portion of the trial to determine the degree to which the IP injected PTX enters the blood stream. Patients will be followed for up to 1 year. Adverse events (AEs) will be captured and graded using the National Cancer Institute’s (NCI) Common Toxicity Criteria for Adverse Events (CTCAE) v. 5.0 (or latest version) and will include the event term, grading, duration and attribution. Attribution to the study agent or intervention will be determined by the PI, Co-PI or treating physician. Clinical trial information: NCT06207305 . [Table: see text]

Event attribution of a midlatitude windstorm using ensemble weather forecasts

The widespread destruction incurred by midlatitude storms every year makes it an imperative to study how storms change with climate. The impact of climate change on midlatitude windstorms, however, is hard to evaluate due to the small signals in variables such as wind speed, as well as the high resolutions required to represent the dynamic processes in the storms. Here, we assess how storm Eunice, which hit the UK in February 2022, was impacted by anthropogenic climate change using the ECMWF ensemble prediction system. This system was demonstrably able to predict the storm, significantly increasing our confidence in its ability to model the key physical processes and their response to climate change. Using modified greenhouse gas concentrations and changed initial conditions for ocean temperatures, we create two counterfactual scenarios of storm Eunice in addition to the forecast for the current climate. We compare the intensity and severity of the storm between the pre-industrial, current, and future climates. Our results robustly indicate that Eunice has become more intense with climate change and similar storms will continue to intensify with further anthropogenic forcing. These results are consistent across forecast lead times, increasing our confidence in them. Analysis of storm composites shows that this process is caused by increased vorticity production through increased humidity in the warm conveyor belt of the storm. This is consistent with previous studies on extreme windstorms. Our approach of combining forecasts at different lead times for event attribution enables combining event specificity and a focus on dynamic changes with the assessment of changing risks from windstorms. Further work is needed to develop methods to adjust the initial conditions of the atmosphere for the use in attribution studies using weather forecasts but we show that this approach is viable for reliable and fast attribution systems.