Minimum Density Hyperplanes

Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning approaches to the classification of unlabelled data. We propose a novel hyperplane classifier for clustering and semi-supervised classification which is motivated by this objective. The proposed minimum density hyperplane minimises the integral of the empirical probability density function along it, thereby avoiding intersection with high density clusters. We show that the minimum density and the maximum margin hyperplanes are asymptotically equivalent, thus linking this approach to maximum margin clustering and semi-supervised support vector classifiers. We propose a projection pursuit formulation of the associated optimisation problem which allows us to find minimum density hyperplanes efficiently in practice, and evaluate its performance on a range of benchmark datasets. The proposed approach is found to be very competitive with state of the art methods for clustering and semi-supervised classification

ErmineJ: Tool for functional analysis of gene expression data sets

BACKGROUND: It is common for the results of a microarray study to be analyzed in the context of biologically-motivated groups of genes such as pathways or Gene Ontology categories. The most common method for such analysis uses the hypergeometric distribution (or a related technique) to look for "over-representation" of groups among genes selected as being differentially expressed or otherwise of interest based on a gene-by-gene analysis. However, this method suffers from some limitations, and biologist-friendly tools that implement alternatives have not been reported. RESULTS: We introduce ErmineJ, a multiplatform user-friendly stand-alone software tool for the analysis of functionally-relevant sets of genes in the context of microarray gene expression data. ErmineJ implements multiple algorithms for gene set analysis, including over-representation and resampling-based methods that focus on gene scores or correlation of gene expression profiles. In addition to a graphical user interface, ErmineJ has a command line interface and an application programming interface that can be used to automate analyses. The graphical user interface includes tools for creating and modifying gene sets, visualizing the Gene Ontology as a table or tree, and visualizing gene expression data. ErmineJ comes with a complete user manual, and is open-source software licensed under the Gnu Public License. CONCLUSION: The availability of multiple analysis algorithms, together with a rich feature set and simple graphical interface, should make ErmineJ a useful addition to the biologist's informatics toolbox. ErmineJ is available from

Anal Cancer debuting as Cancer of Unknown Primary

Anal cancer usually presents with a visible or palpable tumour. In this case we describe a 54-year old man diagnosed with Cancer of Unknown Primary (CUP) with a single inguinal node as the only finding. Thorough examination failed to identify any primary tumour. The patient was treated with lymph node dissection and not until nearly two years after initial diagnosis, was the primary tumour found, and the patient was diagnosed with anal cancer. The patient was treated with chemoradiotherapy and 45 months after initial diagnosis there is still no sign of relapse. This case illustrates, that anal cancer can metastasise before the primary tumour is detectable. Furthermore, it demonstrates the necessity of thorough clinical follow-up after treatment of CUP since the primary tumour was found later. Finally this is a case of a long-term survivor following treatment for metastatic inguinal lymph nodes from an initially unknown primary cancer

Laparoscopic Cholecystectomy for Severe Acute Cholecystitis in a Patient with Situs Inversus Totalis and Posterior Cystic Artery

Situs inversus totalis is an inherited condition characterized by a mirror-image transposition of thoracic and abdominal organs. It often coexists with other anatomical variations. Transposition of the organs imposes special demands on the diagnostic and surgical skills of the surgeon. We report a case of a 34-year-old female patient presented with left upper quadrant pain, signs of acute abdomen, and unknown situs inversus totalis. Severe acute cholecystitis was diagnosed, and an uneventful laparoscopic cholecystectomy was performed. A posterior cystic artery was identified and ligated. Laparoscopic cholecystectomy is feasible in patients with severe acute calculus cholecystitis and situs inversus totalis; however, the surgeon should be alert of possible anatomic variations

Efficient Feature Selection and Multiclass Classification with Integrated Instance and Model Based Learning

Multiclass classification and feature (variable) selections are commonly encountered in many biological and medical applications. However, extending binary classification approaches to multiclass problems is not trivial. Instance-based methods such as the K nearest neighbor (KNN) can naturally extend to multiclass problems and usually perform well with unbalanced data, but suffer from the curse of dimensionality. Their performance is degraded when applied to high dimensional data. On the other hand, model-based methods such as logistic regression require the decomposition of the multiclass problem into several binary problems with one-vs.-one or one-vs.-rest schemes. Even though they can be applied to high dimensional data with L1 or Lp penalized methods, such approaches can only select independent features and the features selected with different binary problems are usually different. They also produce unbalanced classification problems with one vs. the rest scheme even if the original multiclass problem is balanced

Relating Statistical Image Differences and Degradation Features

Document images are degraded through bilevel processes such as scanning, printing, and photocopying. The resulting image degradations can be categorized based either on observable degradation features or on degradation model parameters. The degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The changes in the probability that the characters are from different populations when the model parameters vary correlate with the relationship between observable degradation features and the model parameters. The paper also shows which features have the largest impact on the image

Hierarchically coupled ultradian oscillators generating robust circadian rhythms

Ensembles of mutually coupled ultradian cellular oscillators have been proposed by a number of authors to explain the generation of circadian rhythms in mammals. Most mathematical models using many coupled oscillators predict that the output period should vary as the square root of the number of participating units, thus being inconsistent with the well-established experimental result that ablation of substantial parts of the suprachiasmatic nuclei (SCN), the main circadian pacemaker in mammals, does not eliminate the overt circadian functions, which show no changes in the phases or periods of the rhythms. From these observations, we have developed a theoretical model that exhibits the robustness of the circadian clock to changes in the number of cells in the SCN, and that is readily adaptable to include the successful features of other known models of circadian regulation, such as the phase response curves and light resetting of the phase

Clinical guidelines for caring for women with COVID-19 during pregnancy, childbirth and the immediate postpartum period.

BACKGROUND:The spread of the novel coronavirus (COVID-19) was declared a pandemic by the World Health Organization on 11th March 2020. Since then there has been a rapid rise in development of maternal and perinatal health guidelines related to COVID-19. The aim of this project was to develop a database of Australian and international recommendations relating to antenatal, intrapartum and postpartum care of women during the COVID-19 pandemic, in order to identify inconsistencies in clinical guidance. METHODS:We conducted weekly web searches from 30th March to 15th May 2020 to identify recommendations pertaining to the care of women during pregnancy, labour and postpartum period from national or international professional societies, specialist colleges, Ministries of Health, Australian state and territory governments, and international guideline development organisations. Individual recommendations were extracted and classified according to intervention type, time period, and patient population. Findings were reported using descriptive analysis, with areas of consensus and non-consensus identified. RESULTS:We identified 81 guidelines from 48 different organisations. Generally, there was high consensus across guidelines for specific interventions. However, variable guidance was identified on the use of nitrous oxide during labour, administration of antenatal corticosteroids, neonatal isolation after birth, labour and birth companions, and the use of disease modifying agents for treating COVID-19. CONCLUSION:Discrepancies between different guideline development organisations creates challenges for maternity care clinicians during the COVID-19 pandemic. Collating recommendations and keeping up-to-date with the latest guidance can help clinicians provide the best possible care to pregnant women and their babies