Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring

INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume delineation remains one of the greatest sources of error in the radiotherapy delivery process, which can lead to poor tumour control probability and impact clinical outcome. Contouring assessments are performed to ensure high quality of target volume definition in clinical trials but this can be subjective and labour-intensive. This project addresses the hypothesis that computational segmentation techniques, with a given prior, can be used to develop an image-based tumour delineation process for contour assessments. This thesis focuses on the exploration of the segmentation techniques to develop an automated method for generating reference delineations in the setting of advanced lung cancer. The novelty of this project is in the use of the initial clinician outline as a prior for image segmentation. METHODS: Automated segmentation processes were developed for stage II and III non-small cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed segmentation, two active contour approaches (edge- and region-based) and graph-cut applied on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from normal tissues based on texture features was also investigated. RESULTS: 63 cases were used for development and training. Segmentation and classification performance were evaluated on an independent test set of 16 cases. Edge-based active contour segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07, with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation leakages at the mediastinum were observed. In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and 15.5% were achieved using 16- and 8-pixel regions of interest (ROIs) respectively. Higher misclassification errors of 24.7% and 26.9% for 16- and 8-pixel ROIs were obtained in the analysis of the tumour boundary. CONCLUSIONS: Conventional image-based segmentation techniques with the application of priors are useful in automatic segmentation of tumours, although further developments are required to improve their performance. Texture classification can be useful in distinguishing tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more difficult. Future work with deep-learning segmentation approaches need to be explored.Funded by National Radiotherapy Trials Quality Assurance (RTTQA) grou

Fighting Depression at Christmas

Depression is a hard thing to understand and an even harder thing to explain. But you don’t have to ‘get it’ to help your loved ones this holiday season. Posting about factors that contribute to depression from In All Things - an online hub committed to the claim that the life, death, and resurrection of Jesus Christ has implications for the entire world. http://inallthings.org/fighting-depression-at-christmas

Explainability for Large Language Models: A Survey

Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this paper, we introduce a taxonomy of explainability techniques and provide a structured overview of methods for explaining Transformer-based language models. We categorize techniques based on the training paradigms of LLMs: traditional fine-tuning-based paradigm and prompting-based paradigm. For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge. We also discuss metrics for evaluating generated explanations, and discuss how explanations can be leveraged to debug models and improve performance. Lastly, we examine key challenges and emerging opportunities for explanation techniques in the era of LLMs in comparison to conventional machine learning models

N-doped carbon shell encapsulated PtZn intermetallic nanoparticles as highly efficient catalysts for fuel cells

Abstract(#br)The high cost and poor durability of Pt nanoparticles (NPs) have always been great challenges to the commercialization of proton exchange membrane fuel cells (PEMFCs). Pt-based intermetallic NPs with a highly ordered structure are considered as promising catalysts for PEMFCs due to their high catalytic activity and stability. Here, we reported a facile method to synthesize N-doped carbon encapsulated PtZn intermetallic (PtZn@NC) NPs via the pyrolysis of Pt@Zn-based zeolitic imidazolate framework-8 (Pt@ZIF-8) composites. The catalyst obtained at 800 °C (10%-PtZn@NC-800) was found to exhibit a half-wave potential ( E 1/2 ) up to 0.912 V versus reversible hydrogen electrode (RHE) for the cathodic oxygen reduction reaction in an acidic medium, which shifted by 26 mV positively..

Association between maternal blood lipids levels during pregnancy and risk of small-for-gestational-age infants.

Dyslipidemia in pregnancy are associated with risk of adverse outcomes. As an adverse pregnancy outcome, small-for-gestational-age has been extensively studied in Western countries. However, similar studies have rarely been conducted in Asian countries. Data were derived from 5695 pairs of non-diabetic mothers and neonates between 1 Jan 2014 and 31 Dec 2014. 5.6% neonates in our study were SGA. Serum samples were collected during second and third trimesters for evaluation on fasting lipids levels. The present study intended to explore the associations between maternal lipid profile and small-for-gestational-age neonates. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated and adjusted via logistic regression analysis. After adjustments for confounders, third-trimester total cholesterol levels were associated with a decreased risk for small-for-gestational-age (aOR = 0.622, 95% CI 0.458-0.848, P = 0.002), and third-trimester high-density lipoprotein cholesterol and low-density lipoprotein cholesterol levels were associated with an increased risk for small-for-gestational-age (aOR = 1.955, 95% CI 1.465-2.578, P < 0.001; aOR = 1.403, 95% CI 1.014-1.944, P = 0.041).In the highest gestational weight gain strata, especially the third-trimester, the effect of high-density lipoprotein cholesterol levels on the risk for small-for-gestational-age is larger. High high-density lipoprotein cholesterol level during third trimester could be considered as indicators of a high-risk of small-for-gestational-age, regardless of gestational weight gain

Psoriasis comorbid with atherosclerosis meets in lipid metabolism

Psoriasis (PSO) is a common skin disease affecting approximately 1%–3% of the population, and the incidence rate is increasing yearly. PSO is associated with a dramatically increased risk of cardiovascular disease, the most common of which is atherosclerosis (AS). In the past, inflammation was considered to be the triggering factor of the two comorbidities, but in recent years, studies have found that lipid metabolism disorders increase the probability of atherosclerosis in patients with psoriasis. In this review, we discuss epidemiological studies, clinical treatment methods, risk factors, and lipid metabolism of psoriasis and atherosclerosis comorbidities