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Multiple Instance Learning for the Detection of Lymph Node and Omental Metastases in Carcinoma of the Ovaries, Fallopian Tubes and Peritoneum
Get PDFBackground/Objectives: Surgical pathology of tubo-ovarian and peritoneal cancer carries a well-recognised diagnostic workload, partly due to the large amount of non-primary tumour-related tissue requiring assessment for the presence of metastatic disease. The lymph nodes and omentum are almost universally included in such resection cases and contribute considerably to this burden, principally due to volume rather than task complexity. To date, artificial intelligence (AI)-based studies have reported good success rates in identifying nodal spread in other malignancies, but the development of such time-saving assistive digital solutions has been neglected in ovarian cancer. This study aimed to detect the presence or absence of metastatic ovarian carcinoma in the lymph nodes and omentum.
Methods: We used attention-based multiple-instance learning (ABMIL) with a vision-transformer foundation model to classify whole-slide images (WSIs) as either containing ovarian carcinoma metastases or not. Training and validation were conducted with a total of 855 WSIs of surgical resection specimens collected from 404 patients at Leeds Teaching Hospitals NHS Trust.
Results: Ensembled classification from hold-out testing reached an AUROC of 0.998 (0.985–1.0) and a balanced accuracy of 100% (100.0–100.0%) in the lymph node set, and an AUROC of 0.963 (0.911–0.999) and a balanced accuracy of 98.0% (94.8–100.0%) in the omentum set.
Conclusions: This model shows great potential in the identification of ovarian carcinoma nodal and omental metastases, and could provide clinical utility through its ability to pre-screen WSIs prior to histopathologist review. In turn, this could offer significant time-saving benefits and streamline clinical diagnostic workflows, helping to address the chronic staffing shortages in histopathology
Behavior-induced oscillations in epidemic outbreaks with distributed memory: beyond the linear chain trick using numerical methods
Get PDFWe considered a model for an infectious disease outbreak, when the depletion of susceptible individuals is negligible, and assumed that individuals adapt their behavior according to the information they receive about new cases. In line with the information index approach, we supposed that individuals react to past information according to a memory kernel that is continuously distributed in the past. We analyzed equilibria and their stability, with analytical results for selected cases. Thanks to the recently developed pseudospectral approximation of delay equations, we studied numerically the long-term dynamics of the model for memory kernels defined by gamma distributions with a general non-integer shape parameter, extending the analysis beyond what is allowed by the linear chain trick. In agreement with previous studies, we showed that behavior adaptation alone can cause sustained waves of infections even in an outbreak scenario, and notably in the absence of other processes like demographic turnover, seasonality, or waning immunity. Our analysis gives a more general insight into how the period and peak of epidemic waves depend on the shape of the memory kernel and how the level of minimal contact impacts the stability of the behavior-induced positive equilibrium
The influence of ethnicity on frailty in a United Kingdom (UK) population
Get PDFBackground
Frailty is an important and increasing clinical and public health problem. Within the United Kingdom (UK). Most data relating to the occurrence of frailty is derived from Caucasian groups. This study aimed to determine the influence of ethnicity on the occurrence of frailty in a large UK urban conurbation. We also looked at frailty-related risk of severe illness related to COVID-19 infection.
Methods
Using data from the Greater Manchester Health Record (GMCR), we analysed primary care electronic medical records of 534,367 men and women aged 60 years and over who were alive on 1st January 2020. We assessed frailty using an electronic frailty index (eFI) and categorised subjects as fit, mild, moderate, and severe frailty. We used logistic regressions to examine the association between moderate and severe frailty (eFI ≥ 0.25) and ethnicity adjusted with age, sex and area deprivation (as measured using Townsend Index). We also looked among those with a first positive COVID test, the influence of frailty on subsequent admission to the hospital within 28 days.
Results
The majority of subjects were White (84 %), with 4.7 % describing themselves as Asian or Asian British, and 1.3 % Black or Black British. The unadjusted prevalence of moderate to severe frailty (eFI ≥ 0.25) was 22.1 %. Compared to the prevalence of frailty in Whites (22.5 %), the prevalence was higher in those of Asian or Asian British ethnicity (28.1 %) and lower in those of Black/Black British descent (18.7 %). After adjustment for age, gender, and deprivation, the risk of frailty remained higher in Asians (Odds Ratio = 1.61; 95 % Confidence Intervals = 1.56–1.66) and lower in Black British (OR = 0.73; 95 % CI 0.68–0.78) compared to White British. Among those with a positive COVID-19 test, those with frailty were more likely to require admission to the hospital within 28 days (OR = 1.61; 95 % CI = 1.53, 1.69).
Conclusion
There is variation in the occurrence of frailty across Greater Manchester across ethnic groups, with higher frequency among those of Asian or Asian British descent and lower frequency among those of Black or Black British descent. This study has added to our understanding of the way that frailty prevalence maps across communities, in this case in a large European conurbation. Further research is required to understand the causes of ethnic variation in frailty and whether ethnicity influences frailty outcomes
Vortex detection from quantum data
Get PDFQuantum solutions to differential equations represent quantum data—states that contain relevant information about the system's behavior, yet are difficult to analyze. We propose an algorithm for reading out information from such data, where customized quantum circuits enable efficient extraction of flow properties. We concentrate on the process referred to as quantum vortex detection, where specialized operators are developed for pooling relevant features related to vorticity. Specifically, we propose approaches based on sliding windows and quantum Fourier analysis that provide a separation between patches of the flow field with vortex-type profiles. First, we show how contour-shaped windows can be applied, trained, and analyzed sequentially, providing a clear signal to flag the location of vortices in the flow. Second, we develop a parallel window extraction technique, such that signals from different contour positions are coherently processed to avoid looping over the entire solution mesh. We show that Fourier features can be extracted from the flow field, leading to classification of datasets with vortex-free solutions against those exhibiting Lamb-Oseen vortices. Our work exemplifies a successful case of efficiently extracting value from quantum data, and it points to the need for developing appropriate models for quantum data analysis that can be trained on them
Is structural awareness the key to event camera data cleansing for enhancing veracity?
Get PDFNeuromorphic vision sensors, also known as event cameras, offer significant advantages over conventional frame-based cameras, in terms of high dynamic range, low latency, and low power consumption. However, their high sensitivity to illumination changes and asynchronous operation introduces substantial data (typically in the form of false or structure-irrelevant event data) posing challenges to the veracity of the acquired information in downstream vision tasks, such as, object recognition, feature tracking and human action recognition. Traditional cleansing methods for neuromorphic vision sensor event data typically rely on denoising techniques guided by reference-based metrics, which require auxiliary modalities such as Active Pixel Sensor (APS) frames or manual annotations. These references are often unavailable in real-world scenarios. Moreover, existing reference-free metrics generally overlook structural integrity, leading to deceptively high scores when aggressive noise removal results in the loss of meaningful structure. In this paper, we propose the Temporal Structural Event Index (TSEI), a novel, reference-free, structure-aware metric designed to assess the veracity of cleansed neuromorphic vision sensor event data. TSEI integrates temporal structural similarity (TSSM) and contrast normalization within an adaptive segmentation framework to jointly evaluate signal preservation and noise suppression. Experiments on both synthetic and real-world datasets demonstrate that TSEI strongly correlates with structural fidelity and recognition accuracy, outperforming existing metrics in detecting over-cleansing and structural degradation. These findings highlight that structural awareness is a critical factor in enhancing the veracity of neuromorphic event data and ensuring reliable performance in visual recognition tasks
Comparative Analysis of Global Standards in Accelerated Carbonation Testing of Concrete
Get PDFCarbonation is one of the major causes of steel reinforcement corrosion, affecting the durability of concrete. Natural carbonation is a very long process; therefore, to predict the development of concrete carbonation, standards prescribe accelerated carbonation tests, in which specimens are exposed to carbon dioxide (CO₂) in a controlled environment. Although based on the same principle, global standards differ in methodology, e.g., different CO₂ concentrations, relative humidity or size of the specimens. Results of these tests might not be comparable with each other, and therefore, it is difficult to conclude which methodology most accurately predicts long-term carbonation. This diversity in standards highlights the need to study and compare them to identify similarities, differences, and the reasons behind these variations in accelerated carbonation tests worldwide. In this study, 12 standards from various regions were examined for differences in key parameters. Significant variations were found, largely due to climatic differences and testing objectives. A correlation was observed between CO₂ concentration and specimen surface area, and the study also noted that standards specifying higher CO₂ concentrations tend to have shorter test durations compared to those with lower CO₂ concentrations, aiming to accelerate the testing process. The study found that the ISO standard is applicable across diverse climatic conditions, as its flexible temperature and humidity ranges allow adjustment to local environments within the ISO limits
The ICJ and “progressive causes”
No full textThis chapter considers the International Court of Justice’s record when approaching ‘progressive causes’. It investigates how the Court has responded when faced with contentious and advisory
proceedings involving divisive issues of international concern and assesses how its handling of proceedings involving such causes has evolved over time. It addresses three questions: (i) how we ought to understand ‘progress’ and ‘progressiveness’ in the context of international adjudication; (ii) how international courts and tribunals can be said to contribute to the pursuit of progress; and
(iii) the extent to which the Court can be considered an ‘agent’ of progress. In addressing this latter question, the chapter considers the Court’s practice on four topics of global concern: discrimination; self-determination and decolonisation; nuclear disarmament; and environmental protection. It concludes that the Court ought to be understood as a cautiously (and often reluctantly) progressive
institution
When Vegas comes to Wall Street: Associations between stock price volatility and trading frequency among gamblers
Get PDFBoth gambling and trading involve risk-taking in exchange for potential financial gains. In particular, speculative high-risk high-frequency trading closely resembles disordered gambling behaviour by attracting the same individuals who tend to be overconfident, sensation-seekers, and attracted to quick large potential payoffs. We build on these studies via an incentivised experiment, in which we examine how manipulated levels of market volatility affected trading frequency. Gamblers (N=604) were screened based on the existence of household investments and recruited across the four categories of the Problem Gambling Severity Index. The volatility of stocks was manipulated between-participants (high vs. low). Participants traded fictitious stocks and were provided bonuses based on the results of their trading activity (M=US$4.77, range=[0, 16.99]). Participants traded more often in the high-volatility market, and this finding remained robust after controlling for financial literacy, overconfidence, age, and gender. Many investors trade more frequently than personal finance guides advise, and these results suggest that individuals are more likely to commit this error in more volatile markets. Exploratory analyses suggest that the effect of the volatility manipulation was strongest amongst gamblers who were at low-risk of experiencing gambling harms. As they might be otherwise considered low-risk, these individuals could be overlooked by protective gambling interventions yet nonetheless suffer unmitigated financial harms due to unchecked excessive trading
Implementation successes and lessons learnt from a randomised controlled trial of a complex school-based intervention
Get PDFBackground
The UK has seen a recent shift towards children’s mental health being supported and treated in school settings. Several current school-based interventions focus on autism and social skills, with education professional involvement in their delivery increasing. The study of these interventions poses specific implementation challenges. This paper discusses implementation successes and learnings from the I-SOCIALISE research study which delivered and evaluated efficacy of LEGO® based therapy (now Play Brick Therapy) for autistic children and young people delivered in schools. Detailed Methods and results of the trial are reported elsewhere.
Methods
The I-SOCIALISE study was a pragmatic large-scale NIHR-funded cluster randomised controlled trial. Children and young people, their parents/guardians, and schoolteachers or teaching assistants were recruited from mainstream schools in the UK. They completed outcome measures and were randomised to receive either 12-week of LEGO® based therapy and usual support or usual support only. Various methods to achieve successful recruitment and retention were used and learnings were documented.
Results
The study recruited to time and target with successful delivery of this complex intervention in schools. Several lessons were learnt about recruitment methods, data collection, participant burden and retention, blinding, and the importance of relationships with key school contacts. Main recommendations based on these learnings are provided.
Conclusions
This study demonstrated that it is possible to undertake large scale, robust evaluation of pragmatically delivered complex school-based interventions. Recommendations are made to address the logistical challenges of undertaking research in this setting which are intended to facilitate future research
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