Clustering Pseudo Time Series: Exploring Trajectories in the Ageing Process.

Investigating the natural ageing process typically involves the use of extensive longitudinal datasets that can capture changes associated with the progression of ageing. However, they are often resource-intensive and time-consuming to conduct. Cross-sectional data, on the other hand, provides a snapshot of a population at many different ages and can capture many disease processes but do not incorporate the time dimension.

Identifying and handling data bias within primary healthcare data using synthetic data generators.

Advanced synthetic data generators can simulate data samples that closely resemble sensitive personal datasets while significantly reducing the risk of individual identification. The use of these advanced generators holds enormous potential in the medical field, as it allows for the simulation and sharing of sensitive patient data. This enables the development and rigorous validation of novel AI technologies for accurate diagnosis and efficient disease management.

Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning.

The mobilization of large-scale datasets of specimen images and metadata through herbarium digitization provide a rich environment for the application and development of machine learning techniques. However, limited access to computational resources and uneven progress in digitization, especially for small herbaria, still present barriers to the wide adoption of these new technologies. Using deep learning to extract representations of herbarium specimens useful for a wide variety of applications, so-called "representation learning," could help remove these barriers.

Generating high-fidelity synthetic patient data for assessing machine learning healthcare software.

There is a growing demand for the uptake of modern artificial intelligence technologies within healthcare systems. Many of these technologies exploit historical patient health data to build powerful predictive models that can be used to improve diagnosis and understanding of disease. However, there are many issues concerning patient privacy that need to be accounted for in order to enable this data to be better harnessed by all sectors.

Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records.

Temporal phenotyping enables clinicians to better understand observable characteristics of a disease as it progresses. Modelling disease progression that captures interactions between phenotypes is inherently challenging. Temporal models that capture change in disease over time can identify the key features that characterize disease subtypes that underpin these trajectories.

Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease.

Disease subtyping, which helps to develop personalized treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if we can identify subclasses of disease, then it will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. This paper proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias.

Experts integrate explicit contextual priors and environmental information to improve anticipation efficiency.

The understanding of how experts integrate prior situation-specific information (i.e., ) with emergent visual information when performing dynamic and temporally constrained tasks is limited.

Construction of a network describing asparagine metabolism in plants and its application to the identification of genes affecting asparagine metabolism in wheat under drought and nutritional stress.

A detailed network describing asparagine metabolism in plants was constructed using published data from Arabidopsis () maize (), wheat (), pea (), soybean (), lupin (), and other species, including animals. Asparagine synthesis and degradation is a major part of amino acid and nitrogen metabolism in plants. The complexity of its metabolism, including limiting and regulatory factors, was represented in a logical sequence in a pathway diagram built using yED graph editor software.

Identification of cancer biomarkers of prognostic value using specific gene regulatory networks (GRN): a novel role of RAD51AP1 for ovarian and lung cancers.

To date, microarray analyses have led to the discovery of numerous individual 'molecular signatures' associated with specific cancers. However, there are serious limitations for the adoption of these multi-gene signatures in the clinical environment for diagnostic or prognostic testing as studies with more power need to be carried out. This may involve larger richer cohorts and more advanced analyses.

Reprint of "Updating Markov models to integrate cross-sectional and longitudinal studies".

Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. Cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process.

Updating Markov models to integrate cross-sectional and longitudinal studies.

Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. Cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process.

Impact of e-publication changes in the International Code of Nomenclature for algae, fungi and plants (Melbourne Code, 2012) - did we need to "run for our lives"?

Background: At the Nomenclature Section of the XVIII International Botanical Congress in Melbourne, Australia (IBC), the botanical community voted to allow electronic publication of nomenclatural acts for algae, fungi and plants, and to abolish the rule requiring Latin descriptions or diagnoses for new taxa. Since the 1st January 2012, botanists have been able to publish new names in electronic journals and may use Latin or English as the language of description or diagnosis.

Results: Using data on vascular plants from the International Plant Names Index (IPNI) spanning the time period in which these changes occurred, we analysed trajectories in publication trends and assessed the impact of these new rules for descriptions of new species and nomenclatural acts.

Loss of Merlin induces metabolomic adaptation that engages dependence on Hedgehog signaling.

The tumor suppressor protein Merlin is proteasomally degraded in breast cancer. We undertook an untargeted metabolomics approach to discern the global metabolomics profile impacted by Merlin in breast cancer cells. We discerned specific changes in glutathione metabolites that uncovered novel facets of Merlin in impacting the cancer cell metabolome.

Employer Expectations for Newly Trained Pathologists: Report of a Survey From the Graduate Medical Education Committee of the College of American Pathologists.

Context: -Multiple sources have identified challenges that training programs face in preparing graduates for the "real world" practice of pathology, and many training programs have sought to decrease the gap between skills acquired during training and those required in practice. However, there exists the possibility that some of the difficulty experienced by newly trained pathologists and employers might arise from differences between employer expectations of new hires and what applicants expect from their first job.

Objective: -To define the constellation of skills and attributes employers prioritize when hiring newly trained pathologists.

Integrating Gene Regulatory Networks to identify cancer-specific genes.

Consensus approaches have been widely used to identify Gene Regulatory Networks (GRNs) that are common to multiple studies. However, in this research we develop an application that semi-automatically identifies key mechanisms that are specific to a particular set of conditions. We analyse four different types of cancer to identify gene pathways unique to each of them.

Assessment and Management of Professionalism Issues in Pathology Residency Training: Results From Surveys and a Workshop by the Graduate Medical Education Committee of the College of American Pathologists.

Professionalism issues are common in residency training and can be very difficult to recognize and manage. Almost one-third of the milestones for pathology recently instituted by the Accreditation Council for Graduate Medical Education encompass aspects of professionalism. Program directors are often unsure of how and when to remediate residents for unprofessional behavior.

Discovering study-specific gene regulatory networks.

Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust.

microRNA-29 negatively regulates EMT regulator N-myc interactor in breast cancer.

Background: N-Myc Interactor is an inducible protein whose expression is compromised in advanced stage breast cancer. Downregulation of NMI, a gatekeeper of epithelial phenotype, in breast tumors promotes mesenchymal, invasive and metastatic phenotype of the cancer cells. Thus the mechanisms that regulate expression of NMI are of potential interest for understanding the etiology of breast tumor progression and metastasis.

Exploring early glaucoma and the visual field test: classification and clustering using Bayesian networks.

Bayesian networks (BNs) are probabilistic models used for classification and clustering in several fields. Their ability to deal with unobserved variables and to integrate data and expert knowledge make them an appropriate technique for modeling eye functionality measurements in glaucoma. In this study, a set of BNs is used to simultaneously perform classification of early glaucoma and cluster data into different stages of disease.

Improving predictive models of glaucoma severity by incorporating quality indicators.

Objective: In this paper we present an evaluation of the role of reliability indicators in glaucoma severity prediction. In particular, we investigate whether it is possible to extract useful information from tests that would be normally discarded because they are considered unreliable.

Methods: We set up a predictive modelling framework to predict glaucoma severity from visual field (VF) tests sensitivities in different reliability scenarios.

Halicephalobus gingivalis: a rare cause of fatal meningoencephalomyelitis in humans.

The genus Halicephalobus consists of eight species of free-living nematodes. Only one species (H. gingivalis) has been reported to infect vertebrates.

Modelling and analysing the dynamics of disease progression from cross-sectional studies.

Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies give us a 'snapshot' of this disease process over a large number of people but do not allow us to model the temporal nature of disease, thereby allowing for modelling detailed prognostic predictions. The aim of this paper is to explore an extension of the temporal bootstrap to identify intermediate stages in a disease process and sub-categories of the disease exhibiting subtly different symptoms.