Diversity and generalisation error in classification ensembles

Ensembles are important tools in machine learning because they are often more accurate than single predictors. Although it has been shown that an accurate ensemble would benefit from having both accurate and diverse predictors, some studies in the literature could not support the influence that diversity has on the overall accuracy of an ensemble. In this thesis we are investigating the influence that diversity has on improving accuracy or equivalently reducing the generalisation error. There have been many diversity measures introduced in the literature, however as outlined in [1] the only one that had a strong negative correlation with generalisation error, was a diversity measure called ambiguity. The ambiguity measure was obtained by using the bias-variance decomposition of classifiers along with the 0-1 loss. As a result, our first set of experiments focuses on this type of diversity measure. We analyse the effect that the ambiguity measure has on decreasing the generalisation error of forests created by bootstrapping. We compare the effect of the ambiguity by having bootstrapping with or without replacement, by varying the number of trees, by varying the patterns or features used in building each tree. Our results show that bootstrapping without replacement yields lower test errors. A similar effect has been seen on bigger ensembles or by providing more data to the classifiers. We propose pruning approaches that involve ambiguity and compare their effect on the generalisation error versus a pruning method that promotes randomness. Our results show that there is no significant difference between the two types of approaches. Next, we define two new ambiguity measures derived from the cross entropy and hinge loss. We analyse their properties and find that out of the three ambiguity measures defined for classifiers (including the 0-1 loss introduced earlier), the only one that achieves all the desired properties of a diversity measure is the one obtained from the cross entropy (being always positive, and zero if and only if all the classifiers agree). We build ensembles by using bagging and by varying the sampling rates, we find that there is a negative correlation between generalisation error and diversity at high sampling rates; conversely generalisation error is positively correlated with diversity when the sampling rate is low and the diversity high. We use an evolutionary algorithm in order to maximise ambiguity and we find that the evolved ensemble in general has lower generalisation error than the initial ensemble. We define the term “ambiguous ensembles” as ensembles with high values of ambiguity. Additionally, we investigate the effect of pruning on larger ensembles and propose several pruning methods that prioritize ambiguity, as well as others that promote less ambiguous ensembles. Our results show that the approaches the prefer ambiguous ensembles reduce the generalisation error. Hence, our overall results support the influence that the diversity has on minimising generalisation error. Finally, we define diverse forests by building trees with different impurities. We choose families of impurities which are characterized by different parameters and we analyse the effect of choosing different parameters has on the generalisation performance. By tuning the parameters we can define symmetric or asymmetric impurities. In the case of imbalanced datasets the use of asymmetric impurities has been proven beneficial in predicting the minority class which usually is of big interest. We contrast the behaviour of the forests by using symmetric, asymmetric impurities with forests of trees built with different impurities (different parameters). Our results do not show a significant difference in performance

Spheroid arrays for high-throughput single-cell analysis of spatial patterns and biomarker expression in 3D

We describe and share a device, methodology and image analysis algorithms, which allow up to 66 spheroids to be arranged into a gel-based array directly from a culture plate for downstream processing and analysis. Compared to processing individual samples, the technique uses 11-fold less reagents, saves time and enables automated imaging. To illustrate the power of the technology, we showcase applications of the methodology for investigating 3D spheroid morphology and marker expression and for in vitro safety and efficacy screens. Firstly, spheroid arrays of 11 cell-lines were rapidly assessed for differences in spheroid morphology. Secondly, highly-positive (SOX-2), moderately-positive (Ki-67) and weakly-positive (βIII-tubulin) protein targets were detected and quantified. Third, the arrays enabled screening of ten media compositions for inducing differentiation in human neurospheres. Lastly, the application of spheroid microarrays for spheroid-based drug-screens was demonstrated by quantifying the dose-dependent drop in proliferation and increase in differentiation in etoposide-treated neurospheres

High-throughput spheroid screens using volume, resazurin reduction and acid phosphatase activity

Mainstream adoption of physiologically-relevant three-dimensional models has been slow in the last 50 years due to long, manual protocols with poor reproducibility, high price and closed commercial platforms. This chapter describes high-throughput, low-cost, open methods for spheroid viability assessment which use readily-available reagents and open-source software to analyse spheroid volume, metabolism and enzymatic activity. We provide two ImageJ macros for automated spheroid size determination - for both single images and for images in stacks. We also share an Excel template spreadsheet allowing users to rapidly process spheroid size data, analyse plate uniformity (such as edge effects and systematic seeding errors), detect outliers and calculate dose-response. The methods would be useful to researchers in preclinical and translational research planning to move away from simplistic monolayer studies and explore 3D spheroid screens for drug safety and efficacy without substantial investment in money or time

Claudin 4 Is Differentially Expressed between Ovarian Cancer Subtypes and Plays a Role in Spheroid Formation

Claudin 4 is a cellular adhesion molecule that is frequently overexpressed in ovarian cancer and other epithelial cancers. In this study, we sought to determine whether the expression of claudin 4 is associated with outcome in ovarian cancer patients and may be involved in tumor progression. We examined claudin 4 expression in ovarian cancer tissues and cell lines, as well as by immunohistochemical staining of tissue microarrays (TMAs; n = 500), spheroids present in patients’ ascites, and spheroids formed in vitro. Claudin 4 was expressed in nearly 70% of the ovarian cancer tissues examined and was differentially expressed across ovarian cancer subtypes, with the lowest expression in clear cell subtype. No association was found between claudin 4 expression and disease-specific survival in any subtype. Claudin 4 expression was also observed in multicellular spheroids obtained from patients’ ascites. Using an in vitro spheroid formation assay, we found that NIH:OVCAR5 cells treated with shRNA against claudin 4 required a longer time to form compact spheroids compared to control NIH:OVCAR5 cells that expressed high levels of claudin 4. The inability of the NIH:OVCAR5 cells treated with claudin 4 shRNA to form compact spheroids was verified by FITC-dextran exclusion. These results demonstrate a role for claudin 4 and tight junctions in spheroid formation and integrity

Congenital diaphragmatic hernia and retinoids: searching for an etiology

Congenital diaphragmatic hernia (CDH) is a major life-threatening cause of respiratory failure in the newborn. Recent data reveal the role of a retinoid-signaling pathway disruption in the pathogenesis of CDH. We describe the epidemiology and pathophysiology of human CDH, the metabolism of retinoids and the implications of retinoids in the development of the diaphragm and lung. Finally, we describe the existing evidence of a disruption of the retinoid-signaling pathway in CDH

Cell–cell and cell–matrix dynamics in intraperitoneal cancer metastasis

The peritoneal metastatic route of cancer dissemination is shared by cancers of the ovary and gastrointestinal tract. Once initiated, peritoneal metastasis typically proceeds rapidly in a feed-forward manner. Several factors contribute to this efficient progression. In peritoneal metastasis, cancer cells exfoliate into the peritoneal fluid and spread locally, transported by peritoneal fluid. Inflammatory cytokines released by tumor and immune cells compromise the protective, anti-adhesive mesothelial cell layer that lines the peritoneal cavity, exposing the underlying extracellular matrix to which cancer cells readily attach. The peritoneum is further rendered receptive to metastatic implantation and growth by myofibroblastic cell behaviors also stimulated by inflammatory cytokines. Individual cancer cells suspended in peritoneal fluid can aggregate to form multicellular spheroids. This cellular arrangement imparts resistance to anoikis, apoptosis, and chemotherapeutics. Emerging evidence indicates that compact spheroid formation is preferentially accomplished by cancer cells with high invasive capacity and contractile behaviors. This review focuses on the pathological alterations to the peritoneum and the properties of cancer cells that in combination drive peritoneal metastasis

Pharmacological adjuncts to stop bleeding: options and effectiveness

Severe trauma and massive haemorrhage represent the leading cause of death and disability in patients under the age of 45 years in the developed world. Even though much advancement has been made in our understanding of the pathophysiology and management of trauma, outcomes from massive haemorrhage remain poor. This can be partially explained by the development of coagulopathy, acidosis and hypothermia, a pathological process collectively known as the “lethal triad” of trauma. A number of pharmacological adjuncts have been utilised to stop bleeding, with a wide variation in the safety and efficacy profiles. Antifibrinolytic agents in particular, act by inhibiting the conversion of plasminogen to plasmin, therefore decreasing the degree of fibrinolysis. Tranexamic acid, the most commonly used antifibrinolytic agent, has been successfully incorporated into most trauma management protocols effectively reducing mortality and morbidity following trauma. In this review, we discuss the current literature with regard to the management of haemorrhage following trauma, with a special reference to the use of pharmacological adjuncts. Novel insights, concepts and treatment modalities are also discussed