The bimodality of the 10k zCOSMOS-bright galaxies up to z ~ 1: a new statistical and portable classification based on the optical galaxy properties

Our goal is to develop a new and reliable statistical method to classify galaxies from large surveys. We probe the reliability of the method by comparing it with a three-dimensional classification cube, using the same set of spectral, photometric and morphological parameters.We applied two different methods of classification to a sample of galaxies extracted from the zCOSMOS redshift survey, in the redshift range 0.5 < z < 1.3. The first method is the combination of three independent classification schemes, while the second method exploits an entirely new approach based on statistical analyses like Principal Component Analysis (PCA) and Unsupervised Fuzzy Partition (UFP) clustering method. The PCA+UFP method has been applied also to a lower redshift sample (z < 0.5), exploiting the same set of data but the spectral ones, replaced by the equivalent width of H$\alpha$. The comparison between the two methods shows fairly good agreement on the definition on the two main clusters, the early-type and the late-type galaxies ones. Our PCA-UFP method of classification is robust, flexible and capable of identifying the two main populations of galaxies as well as the intermediate population. The intermediate galaxy population shows many of the properties of the green valley galaxies, and constitutes a more coherent and homogeneous population. The fairly large redshift range of the studied sample allows us to behold the downsizing effect: galaxies with masses of the order of $3\cdot 10^{10}$ Msun mainly are found in transition from the late type to the early type group at $z>0.5$, while galaxies with lower masses - of the order of $10^{10}$ Msun - are in transition at later epochs; galaxies with $M <10^{10}$ Msun did not begin their transition yet, while galaxies with very large masses ($M > 5\cdot 10^{10}$ Msun) mostly completed their transition before $z\sim 1$.Comment: 16 pages, 14 figures, accepted for publication in A&

Observational study of the development and evaluation of a fertility preservation patient decision aid for teenage and adult women diagnosed with cancer: The Cancer, Fertility and Me research protocol

Introduction: Women diagnosed with cancer and facing potentially sterilising cancer treatment have to make time-pressured decisions regarding fertility preservation with specialist fertility services whilst undergoing treatment of their cancer with oncology services. Oncologists identify a need for resources enabling them to support women’s fertility preservation decisions more effectively; women report wanting more specialist information to make these decisions. The overall aim of the ‘Cancer, Fertility and Me’ study is to develop and evaluate a new evidence-based patient decision aid (ptDA) for women with any cancer considering fertility preservation to address this unmet need. Methods and analysis: This is a prospective mixed-method observational study including women of reproductive age (16 years +) with a new diagnosis of any cancer across two regional cancer and fertility centres in Yorkshire, UK. The research involves three stages. In Stage 1 the aim is to develop the ptDA using a systematic method of evidence synthesis and multidisciplinary expert review of current clinical practice and patient information. In Stage 2, the aim is to assess the face validity of the ptDA. Feedback on its content and format will be ascertained using both questionnaires and interviews with patients, user groups and key stakeholders. Finally, in Stage 3 the acceptability of using this resource when integrated into usual cancer care pathways at the point of cancer diagnosis and treatment planning will be evaluated. This will involve a quantitative and qualitative evaluation of the ptDA in clinical practice. Measures chosen include using count data of the ptDAs administered in clinics and accessed online, decisional and patient-reported outcome measures and qualitative feedback. Quantitative data will be analysed using descriptive statistics, paired sample t tests and confidence intervals; interviews will be analysed using thematic analysis. Ethics and dissemination: Research Ethics Committee approval (Ref: 16/EM/0122) and Health Research Authority approval (Ref: 194751) has been granted. Findings will be published in open access peer-reviewed journals, presented at conferences for academic and health professional audiences, with feedback to health professionals and program managers. The Cancer, Fertility and Me ptDA will be disseminated via a diverse range of open-access media, study and charity websites, professional organisations and academic sources. External endorsement will be sought from the International Patient Decision Aid Standards (IPDAS) Collaboration inventory of ptDAs and other relevant professional organisations e.g. the British Fertility Society. Trial registration number: NCT02753296 (www.clinicaltrials.gov); pre-results

Automatic segmentation of myocardium from black-blood MR images using entropy and local neighborhood information.

By using entropy and local neighborhood information, we present in this study a robust adaptive Gaussian regularizing Chan-Vese (CV) model to segment the myocardium from magnetic resonance images with intensity inhomogeneity. By utilizing the circular Hough transformation (CHT) our model is able to detect epicardial and endocardial contours of the left ventricle (LV) as circles automatically, and the circles are used as the initialization. In the cost functional of our model, the interior and exterior energies are weighted by the entropy to improve the robustness of the evolving curve. Local neighborhood information is used to evolve the level set function to reduce the impact of the heterogeneity inside the regions and to improve the segmentation accuracy. An adaptive window is utilized to reduce the sensitivity to initialization. The Gaussian kernel is used to regularize the level set function, which can not only ensure the smoothness and stability of the level set function, but also eliminate the traditional Euclidean length term and re-initialization. Extensive validation of the proposed method on patient data demonstrates its superior performance over other state-of-the-art methods

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

The impact of Charlson comorbidity index on the functional capacity of COVID-19 survivors: a prospective cohort study with one-year follow-up

Objective: To determine the association between the Charlson comorbidity index (CCI) score after discharge with 6-min walk test (6MWT) 1 year after discharge in a cohort of COVID-19 survivors. Methods: In this prospective study, data were collected from a consecutive sample of patients hospitalized for COVID-19. The CCI score was calculated from the comorbidity data. The main outcome was the distance walked in the 6MWT at 1 year after discharge. Associations between CCI and meters covered in the 6MWT were assessed through crude and adjusted linear regressions. The model was adjusted for possible confounding factors (sex, days of hospitalization, and basal physical capacity through sit-to-stand test one month after discharge). Results: A total of 41 patients were included (mean age 58.8 +/- 12.7 years, 20/21 men/women). A significant association was observed between CCI and 6MWT (meters): (i) crude model: beta = -18.7, 95% CI = -34.7 to -2.6, p < 0.05; (ii) model adjusted for propensity score including sex, days of hospitalization, and sit-to-stand: beta = -23.0, 95% CI = -39.1 to -6.8, p < 0.05. Conclusions: A higher CCI score after discharge indicates worse performance on the 6MWT at 1-year follow-up in COVID-19 survivors. The CCI score could also be used as a screening tool to make important clinical decisions

Identifying aspects of physiotherapy and occupational therapy provision in community palliative rehabilitation that could improve outcomes: A realist review

Background: The provision of physiotherapy and occupational therapy in palliative care is often poorly understood. There is currently no guidance on how to deliver these services in the community, potentially leading to unwarranted variation in practice and unmet patient need. Aim: To identify aspects of physiotherapy and occupational therapy provision in community palliative rehabilitation that could improve outcomes. Design: A realist review of the literature following RAMESES standards, with stakeholder input throughout. Data sources: Iterative literature searches were conducted from September 2023 to April 2024. All relevant data sources relating to delivery of physiotherapy and occupational therapy in community palliative care were included. Results: Forty-two international publications were included, published between 2000 and 2023. Five key aspects were identified: (1) Early referral into community palliative rehabilitation. (2) Layered model, basing level of service on complexity of needs. Within this, clinicians without professional qualifications deliver simple interventions after assessment by a qualified physiotherapist or occupational therapist while specialist clinicians review more complex presentations. Services are cohesive by being integrated with primary care, other community services and specialist medical and palliative care and there is representation of physiotherapists and occupational therapists within leadership teams. (3) Holistic assessments form the backbone of the service with personalised interventions tailored to patients’ needs and goals. (4) Accessible and flexible services are offered to meet patients’ needs throughout their palliative journey. (5) Information and education for patients and carers are available throughout. Conclusions: Integrating these five key aspects of physiotherapy and occupational therapy provision into community palliative rehabilitation could help ensure palliative patients receive the therapy they need

The value of standards for health datasets in artificial intelligence-based applications

Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access. This study aims to explore existing standards, frameworks and best practices for ensuring adequate data diversity in health datasets. Exploring the body of existing literature and expert views is an important step towards the development of consensus-based guidelines. The study comprises two parts: a systematic review of existing standards, frameworks and best practices for healthcare datasets; and a survey and thematic analysis of stakeholder views of bias, health equity and best practices for artificial intelligence as a medical device. We found that the need for dataset diversity was well described in literature, and experts generally favored the development of a robust set of guidelines, but there were mixed views about how these could be implemented practically. The outputs of this study will be used to inform the development of standards for transparency of data diversity in health datasets (the STANDING Together initiative)

Successful Completion of the Top-off Upgrade of the Advanced Light Source

An upgrade of the Advanced Light Source to enable top-off operation has been completed during the last four years. The final work centered around radiation safety aspects, culminating in a systematic proof that top-off operation is equally safe as decaying beam operation. Commissioning and transition to full user operations happened in late 2008 and early 2009. Top-off operation at the ALS provides a very large increase in time-averaged brightness (by about a factor of 10) as well as improvements in beam stability. The following sections provide an overview of the radiation safety rationale, commissioning results, as well as experience in user operations