Rare Event Research Articles

Risk of Suicide and Suicidality in Patients with Moderate to Severe Psoriasis: results from the British Association of Dermatologists Biologic and Immunomodulators Register (BADBIR).

Psoriasis is associated with poor mental health and reduced quality of life. Although the high risk for depression in patients with psoriasis is well-established, their suicidality risk is uncertain. Previous studies provide contrasting results and have not included patients with clinically-confirmed severe disease. Our aim was to determine risk of suicide among patients with moderate to severe psoriasis compared with the general population, and investigate if psychiatric comorbidity or history of suicidality increases future suicidality risk in psoriasis. We further estimated the incidence of suicidal and self-injurious behaviours in patients. Analysis was performed using the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR). As controls, general population mortality and suicide data were used. There were 9 suicides in BADBIR. The incidence rate of suicide was 12.5 per 100,000 person-years (95% CI 6.53, 24.11) in BADBIR versus 11.0 per 100,000 person-years (95% CI 10.7, 11.3) in the general population in England and Wales. Among patients, psychiatric comorbidity or past suicidality was associated with higher risk for suicidal ideation, suicide attempts and self-injurious behaviours. Suicide rates among patients with moderate to severe psoriasis were not significantly higher compared with the general population. Suicide is a rare event and our results are limited by the uncertainty in the estimate reliability. However, considering the high depression prevalence in psoriasis, our findings support the need for prompt assessment of patients for psychiatric comorbidities and suicidality history. Further research is required on suicidal behaviours and the role of psoriasis severity.

A fast and accurate numerical method for the left tail of sums of independent random variables

We present a flexible, deterministic numerical method for computing left-tail rare events of sums of non-negative, independent random variables. The method is based on iterative numerical integration of linear convolutions by means of Newtons–Cotes rules. The periodicity properties of convoluted densities combined with the Trapezoidal rule are exploited to produce a robust and efficient method, and the method is flexible in the sense that it can be applied to all kinds of non-negative continuous RVs. We present an error analysis and study the benefits of utilizing Newton–Cotes rules versus the fast Fourier transform (FFT) for numerical integration, showing that although there can be efficiency benefits to using FFT, Newton–Cotes rules tend to preserve the relative error better, and indeed do so at an acceptable computational cost. Numerical studies on problems with both known and unknown rare-event probabilities showcase the method’s performance and support our theoretical findings.

Autoantibodies to protein S may explain rare cases of coagulopathy following COVID-19 vaccination.

While Coronavirus disease 2019 (COVID-19) vaccines have proven to be both effective and generally safe, rare but severe adverse events following immunization (AEFIs) are described. Autoantibodies to platelet factor-4 are associated with catastrophic thrombotic AEFIs, but comprehensive investigations of other autoantibodies are lacking. We aimed to detect and describe autoantibodies targeting coagulation-related proteins in a population-wide cohort (SWEDEGENE) including AEFIs attributed to COVID-19 vaccines in Sweden. Subjects were recruited from December 2020 to October 2022 and were stratified based on diagnosis and COVID-19 exposure. Screening was carried out in two phases, with a multiplex bead-based assay in the first subset (until September 2021) and with targeted assays for the second (until October 2022). Positivity was defined based on absolute, relative, and biological/technical thresholds. Patients with coagulation-related AEFIs were older and the Vaxzevria vaccine was overrepresented in this group. Two cases had antiphospholipid antibodies but none had PF4 antibodies. We identified six positives for protein S autoantibodies. Protein S concentrations were negatively correlated with autoantibody response in patients with immunoreactivity and functional analysis revealed low protein S activity in three subjects. Our population-wide analysis reveals cases with autoantibodies against protein S which possibly underlie coagulopathic AEFIs.

Radiation-Induced Breast Angiosarcoma-A Single-Institution Experience.

Introduction: Radiation-induced breast angiosarcoma (RIBAS) is a rare adverse event associated with postoperative breast irradiation. The data from the literature indicate that RIBAS occurs in less than 0.3% of patients treated with adjuvant radiotherapy for breast cancer. Given the rarity, diverse clinical presentation, poor prognosis, and lack of consensus on the management, this study aimed to present experiences of our specialized cancer center with RIBAS, in terms of the incidence, presentation, management, and outcomes. Methods: We reviewed the medical records of 10,834 breast cancer patients treated at the Institute for Oncology and Radiology of Serbia between January 2013 and June 2024 to detect patients that had breast-conserving surgery, followed by postoperative irradiation, and developed angiosarcoma in the irradiated area at least 3 years after radiotherapy, without distant metastases. The incidence, latency period, management, and treatment outcomes were analyzed. Results: A total of nine female patients with RIBAS were identified and included in this study. The median age at RIBAS diagnosis was 64 years (range: 36-68), with a median latency of 64 months (95% CI > 57) from irradiation to diagnosis. The mean tumor size was 55 mm (SD 32.78). Patients were followed for a median of 30 months (range: 7-40) after initial RIBAS surgery. Local recurrence occurred in seven patients (77.8%), with five undergoing re-do surgery with curative intent. Three patients developed distant metastases during follow-up. The median overall survival (OS) was 31 months (95% CI > 30), with a 3-year survival rate of 15.2% (95% CI 2.5-91.6%). The median local recurrence-free interval was 10 months (95% CI > 3). Median OS after RIBAS local recurrence and after breast cancer treatment was 17 months (95% CI > 15) and 108 months (95% CI > 88), respectively. Conclusions: RIBAS is a rare but increasingly prevalent adverse event associated with BC irradiation, marked by an aggressive disease course and high relapse rates. Awareness, prompt diagnosis, and a radical surgical approach with wide clear margins are critical for improving patients' outcomes.

Autism Spectrum Disorder and Associated Factors in Children in Spain, 2017: Population-Based Cross-Sectional Study.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with biological, multicausal and polygenic origins. The true prevalence of ASD has not been clearly established. The aim of this study was to estimate the prevalence of ASD in children aged 3 to 14 years in Spain and to analyze the factors associated with it. A cross-sectional observational study using data from the 2017 National Survey of Health in Minors in Spain. Primary outcome was the diagnosis of ASD, and sociodemographic, behavioral, health-related, the use of health services, household and medication use variables was analyzed. Multivariable logistic regression model with a correction for modelling rare events was fitted. Complex sampling was undertaken, using the survey elevation factor in the analysis. A total of 4409 children were included, and there were 26 children with ASD, for a prevalence of 0.59%, representing 29,143 children with ASD. Factors significantly associated were male sex, having visited a psychologist, and/or a speech therapist in the past year, presenting probable problems with peers, antisocial behavior, taking antibiotics and taking other medications. The findings of this study may be useful to inform health policies and develop strategic plans to identify and address the needs of children with ASD.

Homicide-Suicides in Pakistan: An analysis of Newspaper reports of two years

Background and Objective: Our understanding of homicide-suicide (H-S), a rare yet tragic event, is sparse. While the phenomenon has been studied in the West for many years, only limited literature is available from Asia and none to our knowledge from Pakistan. There is evidence of complexity of the interaction between cultural, societal, and psychological mechanisms underlying this phenomenon; therefore, research findings from the Western countries cannot be directly applied in non-Western societies. Our objective was to study homicide-suicides in Pakistan, describe the characteristics of offenders and victims, determine the types of H-S, and examine possible motives and any events prior to the offense. Methods: In the absence of any official data on homicide-suicides, we used newspaper surveillance approach of four most widely circulated Pakistani newspapers (one Urdu and three English Daily) for two years (1st January 2019 to 31st December 2020). Each case was categorized using the modified Marzuk et al., Tardiff, and Hirsch’s classification of homicide-suicides. Descriptive statistics were used to analyze the data that was then compared with published literature. Results: There were 114 H-S incidents with 198 victims of homicide during the study period, reported in the newspapers. Familial H-S particularly filicide-suicide were predominant, followed by spousal/ consortial H-S. Forty-one (36%) H-S involved multiple victims. The perpetrators across all categories of H-S were predominantly male (67%); the victims were predominantly women and children. Firearms were used in most incidents. The primary motive for the majority of H-S cases was familial, financial, and social stressors. Conclusions: The study highlights several unique patterns (predominance of familicide, multiple victims including high proportion of children) and a series of vulnerabilities (incidents related mostly to familial/ financial and social stressors) that overlap each other and ultimately lead to this tragic end. There is need to increase our understanding and develop effective evidence-based prevention strategies for H-S in Pakistan. It is also very important to have a national surveillance network and national violent death reporting system in the country for studying H-S cases, and for evaluating the impact of prevention programs. doi: https://doi.org/10.12669/pjms.40.10.9812 How to cite this: Imran N, Ayub M, Haider II, Rafiq B, Tahir SM, Naveed S, et al. Homicide-Suicides in Pakistan: An analysis of Newspaper reports of two years. Pak J Med Sci. 2024;40(10):2196-2201. doi: https://doi.org/10.12669/pjms.40.10.9812 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Perspective on donor-derived infections in Italy.

Expected and unexpected donor-derived infections (DDI) are a rare event in solid organ transplant (SOT) recipients but are potentially associated with significant morbidity and mortality. To assure the microbial safety of transplantation, both national guidelines and the current, regional, and local epidemiology of infectious diseases must be considered. In the present paper the strategies adopted by the Italian National Center for Transplantation (CNT) since 2003 and their evolution to guarantee the safety of organ transplantation are reported. Starting in 2012 mandatory reporting to the CNT of all adverse reactions was started. The number and type of DDI reported to the CNT are currently being analyzed. The infectious diseases second opinion has written and updated the guidelines on the safety of organs for transplantation and supported the Italian transplant network for the use of organs from donors with suspected or documented potentially transmissible infections. A transplant safety network was developed in Italy in 2003 and has been updated according to the evolving knowledge and the changing epidemiology. This is an evolving field, and a continuous update of the recommendation is needed.

Rare Adult acute leukemia with Coexisting myeloblastic (basophilic differentiation) & B-lymphoblastic subpopulations Case Study

Abstract Introduction/Objective Acute basophilic leukemia is a very rare subgroup of acute myeloid leukemia that is defined in the 2022 WHO classification by presence of at least 20% blast cells in peripheral blood or bone marrow with immature and maturing cells of basophil lineage. The simultaneous occurrence of two hematological malignancies in a middle- aged adult patient is quite a rare event as evidenced by the rare cases reported in the literature. To our knowledge, this is the first case to be reported with concomitant acute basophilic and B-lymphoblastic leukemia in adult patients. these findings necessitates the comprehensive systematic approach to attain accurate diagnosis and plan the appropriate treatment protocol. Methods/Case Report Bone marrow aspiration & biopsy procedure was performed under local anesthesia and marrow samples were subjected to: lieshman staining of marrow films and direct examination. Immunophenotypic analysis of marrow aspirate by FACS Canto II flow cytometer. Cytogenetics studies were done as karyotyping and gene mutation detection by FISH. Results (if a Case Study enter NA) NA Conclusion Morphology of marrow films: 50% Blast cells, group of them are medium to larg-cells with high nucleocytoplasmic ratio and immature chromatin and the other group shows blast cells with sparse basophilic granulations, plus 20% immatue and maturing basophils. IPT shows two populations: the Myeloblast population (basophilic differentiation) is Positive for CD45dim, CD34het, CD13, CD33, CD117prt, CD25het, CD123het, cyt MPO part, HLA-DR partial. the B lineage lymphoblast population is Positive for CD45dim, CD34het, CD19, CD79a, TdT, CD20het, CD38. Cytogenetics studies: hyperploidy 56, (X, Y, 9, 10, 12, 13, 15, 18, 22, mar+ extra-chromosomes) FISH: +ve for FLT3-ITD mutation. the picture is by morphology & IPT consistent with biclonal leukemia, myeloid clone (acute basophilic leukemia) & ProB-ALL. The systematic laboratory approach from careful morphological examination of peripheral blood and bone marrow smears to confirmatory immunophenotypic analysis of marrow specimens by flow cytometry, and the comprehensive cytogenetics analysis, has a vital role in diagnosing such rare and aggressive phenomenon and provide a good chance for planning the appropriate treatment protocol with critical timesaving and cost effectiveness.

Effectiveness and Safety of Dupuytren Contracture Treatments: A Systematic Review and Meta-Analysis Using the GRADE Approach.

There is currently no consensus on the optimal treatment for Dupuytren contracture. Prior meta-analyses have been limited by suboptimal data synthesis methodologies. We conducted an updated evidence review comparing the effectiveness and safety of percutaneous needle fasciotomy (PNF), collagenase clostridium histolyticum (CCH), and limited fasciectomy (LF) using the GRADE approach. CENTRAL, MEDLINE, and Embase were searched for randomized controlled trials comparing outcomes following PNF, CCH, and LF for Dupuytren contracture treatment. Outcomes of interest included residual contracture, recurrence rate, hand function, pain, global satisfaction, and adverse events. Time points included 3-months, 1-year, and 2-3 years. Seventeen publications (1,010 patients) were included. High to moderate certainty evidence showed no clinically important difference in long-term contracture reduction (PNF vs. LF (mean difference (MD): 7.6°; 95% CI: 1.8°-13.4°), CCH vs. LF (MD: 4.8°; 95% CI: -1.3°-10.9°)). Moderate certainty evidence indicated that LF provides the lowest risk of long-term recurrence (PNF vs. LF (relative risk (RR): 12.3; 95% CI: 1.6-92.4), CCH vs. LF (RR: 9.5; 95% CI 1.2-73.4)), LF has a higher risk of serious adverse events than PNF (RR: 0.5; 95% CI 0.3-0.9), and CCH has a higher risk of overall adverse events than PNF (RR: 4.8; 95% CI 2.9-7.0). CCH, PNF, and LF are equally effective in long-term contracture reduction. However, LF yields more durable results at a higher risk of rare but serious adverse events. Current evidence suggests the use of PNF over CCH. However, ultimate treatment decisions should be tailored to individual patient preferences.

A new Bayesian method for estimation of value at risk and conditional value at risk

AbstractValue at Risk (VaR) and Conditional Value at Risk (CVaR) have become the most popular measures of market risk in Financial and Insurance fields. However, the estimation of both risk measures is challenging, because it requires the knowledge of the tail of the distribution. Therefore, Extreme Value Theory initially seemed to be one of the best tools for this kind of problems, because using peaks-over-threshold method, we can assume the tail data approximately follow a Generalized Pareto distribution (GPD). The main objection to its use is that it only employs observations over the threshold, which are usually scarce. With the aim of improving the inference process, we propose a new Bayesian method that computes estimates built with all the information available. Informative prior Bayesian (IPB) method employs the existing relations between the parameters of the loss distribution and the parameters of the GPD that models the tail data to define informative priors in order to perform Metropolis–Hastings algorithm. We show how to apply IPB when the distribution of the observations is Exponential, stable or Gamma, to make inference and predictions. .Afterwards, we perform a thorough simulation study to compare the accuracy and precision of the estimates computed by IPB and the most employed methods to estimate VaR and CVaR. Results show that IPB provides the most accurate, precise and least biased estimates, especially when there are very few tail data. Finally, data from two real examples are analysed to show the practical application of the method.

From online hate speech to offline hate crime: the role of inflammatory language in forecasting violence against migrant and LGBT communities

Social media messages often provide insights into offline behaviors. Although hate speech proliferates rapidly across social media platforms, it is rarely recognized as a cybercrime, even when it may be linked to offline hate crimes that typically involve physical violence. This paper aims to anticipate violent acts by analyzing online hate speech (hatred, toxicity, and sentiment) and comparing it to offline hate crime. The dataset for this preregistered study included social media posts from X (previously called Twitter) and Facebook and internal police records of hate crimes reported in Spain between 2016 and 2018. After conducting preliminary data analysis to check the moderate temporal correlation, we used time series analysis to develop computational models (VAR, GLMNet, and XGBTree) to predict four time periods of these rare events on a daily and weekly basis. Forty-eight models were run to forecast two types of offline hate crimes, those against migrants and those against the LGBT community. The best model for migrant crime achieved an R2 of 64%, while that for LGBT crime reached 53%. According to the best ML models, the weekly aggregations outperformed the daily aggregations, the national models outperformed those geolocated in Madrid, and those about migration were more effective than those about LGBT people. Moreover, toxic language outperformed hatred and sentiment analysis, Facebook posts were better predictors than tweets, and in most cases, speech temporally preceded crime. Although we do not make any claims about causation, we conclude that online inflammatory language could be a leading indicator for detecting potential hate crimes acts and that these models can have practical applications for preventing these crimes.

Opportunities and challenges for identifying undiagnosed Rare Disease patients through analysis of primary care records: long QT syndrome as a test case.

Patients with rare genetic diseases frequently experience significant diagnostic delays. Routinely collected data in the electronic health record (EHR) may be used to help identify patients at risk of undiagnosed conditions. Long QT syndrome (LQTS) is a rare inherited cardiac condition associated with significant morbidity and premature mortality. In this study, we examine LQTS as an exemplar disease to assess if clinical features recorded in the primary care EHR can be used to develop and validate a predictive model to aid earlier detection. 1495 patients with an LQTS diagnostic code and 7475 propensity-score matched controls were identified from 10.5million patients' electronic primary care records in the UK's Clinical Practice Research Datalink (CPRD). Associated clinical features recorded before diagnosis (with p < 0.05) were incorporated into a multivariable logistic regression model, the final model was determined by backwards regression and validated by bootstrapping to determine model optimism. The mean age at LQTS diagnosis was 58.4 (SD 19.41). 18 features were included in the final model. Discriminative accuracy, assessed by area under the curve (AUC), was 0.74, (95% CI 0.73, 0.75) (optimism 6%). Features occurring at significantly greater frequency before diagnosis included: epilepsy, palpitations, syncope, collapse, mitral valve disease and irritable bowel syndrome. This study demonstrates the potential to develop primary care prediction models for rare conditions, like LQTS, in routine primary care records and highlights key considerations including disease suitability, finding an appropriate linked dataset, the need for accurate case ascertainment and utilising an approach to modelling suitable for rare events.

A Comprehensive Survey on Rare Event Prediction

Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistical and ML. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches. Specifically, we consider 73 datasets from different modalities (i.e., numerical, image, text, and audio), four major categories of data processing, five major algorithmic groupings, and two broader evaluation approaches. This paper aims to identify gaps in the current literature and highlight the challenges of predicting rare events. It also suggests potential research directions, which can help guide practitioners and researchers.

Nested Sampling for Uncertainty Quantification and Rare Event Estimation

Nested Sampling for Uncertainty Quantification and Rare Event Estimation

Eighteen-year trends in the rates of intra-operative cardiac arrest and associated mortality at a public university hospital in Brazil.

Intra-operative cardiac arrest is a rare but life-threatening event. Over the past two decades, various initiatives have improved the care of patients undergoing surgery at our quaternary teaching hospital in Brazil. We aimed to evaluate the epidemiology of intra-operative cardiac arrest and associated 30-day mortality over an 18-year period. A secondary aim was to identify associated risk factors. We conducted a retrospective observational study using data collected from 1 January 2005 to 31 December 2022. Factors associated with cardiac arrest and mortality were identified using multivariable logistic regression analysis. Among the 154,178 cases, the overall rates of intra-operative cardiac arrest (n = 297) and associated 30-day mortality (n = 248) were 19.3 (95%CI (16.6-21.9)) and 16.1 (95% CI 13.9-18.3) per 10,000 anaesthetics, respectively. These decreased over time (2005-2010 vs. 2017-2022) from 26.3 (95%CI 21.0-31.6) to 15.4 (95%CI 12.0-18.7) per 10,000 anaesthetics, and from 23.4 (95%CI 18.8-28.1) to 13.7 (95%CI 10.8-16.7) per 10,000 anaesthetics, respectively. Factors associated with intra-operative cardiac arrest included children aged < 1 year (adjusted OR (95%CI) 3.51 (1.87-6.57)); ASA physical status 3-5 (adjusted OR (95%CI) 13.85 (8.86-21.65)); emergency surgery (adjusted OR (95%CI) 10.06 (7.85-12.89)); general anaesthesia (adjusted OR (95%CI) 8.79 (4.60-19.64)); surgical procedure involving multiple specialities (adjusted OR (95%CI) 9.13 (4.24-19.64)); cardiac surgery (adjusted OR (95%CI) 7.69 (5.05-11.71)); vascular surgery (adjusted OR (95%CI) 6.21 (4.05-9.51)); and gastrointestinal surgery (adjusted OR (95%CI) 2.98 (1.91-4.65)). We have shown an important reduction in intra-operative cardiac arrest and associated 30-day mortality over an 18-year period. Identification of relative risk factors associated with intra-operative cardiac arrest can be used to improve the safety and quality of patient care, especially in a resource-limited setting.

Double-loop importance sampling for McKean–Vlasov stochastic differential equation

This paper investigates Monte Carlo (MC) methods to estimate probabilities of rare events associated with the solution to the d-dimensional McKean–Vlasov stochastic differential equation (MV-SDE). MV-SDEs are usually approximated using a stochastic interacting P-particle system, which is a set of P coupled d-dimensional stochastic differential equations (SDEs). Importance sampling (IS) is a common technique for reducing high relative variance of MC estimators of rare-event probabilities. We first derive a zero-variance IS change of measure for the quantity of interest by using stochastic optimal control theory. However, when this change of measure is applied to stochastic particle systems, it yields a P×d\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P \ imes d$$\\end{document}-dimensional partial differential control equation (PDE), which is computationally expensive to solve. To address this issue, we use the decoupling approach introduced in (dos Reis et al. 2023), generating a d-dimensional control PDE for a zero-variance estimator of the decoupled SDE. Based on this approach, we develop a computationally efficient double loop MC (DLMC) estimator. We conduct a comprehensive numerical error and work analysis of the DLMC estimator. As a result, we show optimal complexity of OTOLr-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}\\left( \ extrm{TOL}_{\ extrm{r}}^{-4}\\right) $$\\end{document} with a significantly reduced constant to achieve a prescribed relative error tolerance TOLr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{TOL}_{\ extrm{r}}$$\\end{document}. Subsequently, we propose an adaptive DLMC method combined with IS to numerically estimate rare-event probabilities, substantially reducing relative variance and computational runtimes required to achieve a given TOLr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{TOL}_{\ extrm{r}}$$\\end{document} compared with standard MC estimators in the absence of IS. Numerical experiments are performed on the Kuramoto model from statistical physics.

Overtaking risk assessment based on extreme value theory and intelligent network information

In order to evaluate the overtaking risk level of intelligent connected vehicles when providing different connected information, and to make up for the neglect of driver factors in traditional risk assessment and the inadequate evaluation of complex traffic scenarios by a single traffic conflict indicator, this paper introduces the Block maxima (BM) and Peak over threshold (POT) methods to fit the extreme value distribution for two conflict scenarios involved in overtaking events (following vehicle conflict and frontal vehicle conflict), so as to evaluate the risk of following vehicle accidents and frontal collision accidents during overtaking. In each conflict scenario, a non-stationary extreme value model considering driver factors and a binary extreme value model considering different traffic conflict indicators are constructed, and the model is verified by the overtaking test data of intelligent connected vehicles in a two-way two-lane vehicle. Overtaking events were extracted from the original test data and conflict indicators were calculated: including the collision time GAP with the front vehicle at the beginning of the overtaking event, the collision time TTC_t1 with the oncoming vehicle, the deceleration DRAC, and the collision time TTC with the oncoming vehicle and the headway time THW with the front vehicle at the end of the overtaking event. The probability of the event with a negative time conflict indicator or DRAC greater than MADR was used to characterize the degree of collision risk. The results show that in the following vehicle conflict scenario, the binary extreme value model constructed with different conflict indicators has different error results, among which the binary extreme value model constructed with THW&amp;DRAC has the most accurate evaluation result (standard error MAE = 0.00028); in the front vehicle conflict scenario, the binary extreme value model constructed with TTC&amp;DRAC has the most accurate evaluation result ( MAE = 0.006 ). In different conflict scenarios, the non- stationary extreme value model considering the driver factor significantly improves the risk assessment accuracy compared with the model without considering the driver factor (small AIC and BIC values). In addition, different intelligent network information (real-time distance, overtaking advice, speed advice) brings different overtaking risks, and when the intelligent network information is speed advice, the vehicle's overtaking risk is the lowest. Therefore, the non- stationary extreme value model and binary extreme value model considering driver factors proposed in this paper can effectively evaluate driving risks through traffic conflict indicators. Secondly, the experimental data of intelligent networked vehicles show that the extreme value theory model proposed in this paper can accurately evaluate the overtaking risk level of intelligent networked vehicles when providing different network information.

BRCA promoter methylation in triple-negative breast cancer is preserved in xenograft models and represents a potential therapeutic marker for PARP inhibitors.

Most triple-negative breast cancers (TNBC) are sporadic in nature and often associated with dysfunction of the BRCA1 or BRCA2 genes. Since somatic BRCA mutations are rare in breast cancer (BC), this dysfunction frequently is the result of BRCA promoter methylation. Despite the phenotypic similarities of these tumors to those with germline or somatic BRCA mutation, the evidence of response to PARP inhibitors is unclear. We analyzed the prevalence of BRCA promoter methylation in 29 BC metastases through the well-established Illumina Infinium EPIC Human Methylation Bead Chip. In cases with BRCA methylation, the xenograft of the same tumor was tested. Additionally, we compared BC xenografts with an identified BRCA methylation to their matched primary tumors and subsequently investigated the efficacy of PARP inhibitors on tumor organoids from a BRCA2 promoter-methylated BC. BRCA2 promotor hypermethylation was identified in one pleural metastasis of a young patient as well as in the xenograft tissue. We also identified five more xenograft models with BRCA2 promotor hypermethylation. Analysis of one matched primary tumor confirmed the same BRCA2 methylation. PARP inhibitor treatment of tumor organoids derived from the BRCA2 methylated xenograft tumor tissue of the young patient showed a significant decline in cell viability, similar to organoids with somatic BRCA1 mutation, while having no effect on organoids with BRCA1 wildtype. BRCA promotor hypermethylation seems to be a rare event in metastatic BC but is preserved in subsequent xenograft models and might represent an attractive therapeutic marker for PARP inhibitors.

The massive 2016 marine heatwave in the Southwest Pacific: An "El Niño-Madden-Julian Oscillation" compound event.

El Niño typically induces cooling in the Southwest Pacific Ocean during austral summers, usually leading to decreased marine heatwave frequency and severity. However, the 2016 extreme El Niño unexpectedly coincided with the longest and most extensive marine heatwave ever recorded in the region. This heatwave, spanning over 1.7 million square kilometers, persisting for 24 days with a peak intensity of 1.5°C, resulted in massive coral bleaching and fish mortality. This exceptional warming resulted from anomalously strong shortwave radiation and reduced heat loss via latent heat fluxes, owing to low wind speed and increased air humidity. These anomalies are attributed to a rare combined event "Madden-Julian Oscillation and extreme El Niño." Following 10 February, the rapid dissipation of this marine heatwave results from the most intense cyclone ever recorded in the South Pacific. The hazardous ecological impacts of this extreme event highlight the needs for improving our understanding of marine heatwave-driving mechanisms that may result in better seasonal predictions.

Risk Factors for Musculoskeletal Disorders in Korean Farmers: Survey on Occupational Diseases in 2020 and 2022.

This study investigated factors influencing the prevalence of musculoskeletal disorders (MSDs) resulting from agricultural work, utilizing the 2020 and 2022 occupational disease survey data collected by the Rural Development Administration. The combined data from these years indicated a 6.02% prevalence of MSDs, reflecting a significant class imbalance in the binary response variables. This imbalance could lead to classifiers overlooking rare events, potentially inflating accuracy assessments. We evaluated five distinct models to compare their performance using both original and synthetic data and assessing the models' performance based on synthetic data generation. In the multivariate logistic model, we focused on the main effects of the covariates as there were no statistically significant second-order interactions. Focusing on the random over-sampling examples (ROSE) method, gender, age, and pesticide use were particularly impactful. The odds of experiencing MSDs were 1.29 times higher for females than males. The odds increased with age: 2.66 times higher for those aged 50-59, 4.60 times higher for those aged 60-69, and 7.16 times higher for those aged 70 or older, compared to those under 50. Pesticide use was associated with 1.26 times higher odds of developing MSDs. Among body part usage variables, all except wrists and knees were significant. Farmers who frequently used their necks, arms, and waist showed 1.27, 1.11, and 1.23 times higher odds of developing MSDs, respectively. The accuracy of the raw method was high, but the ROSE method outperformed it for precision and F1 score, and both methods showed similar AUC.