States, Processes and Events, and the Ontology of Causal Relations

The subject of causality is large, and fraught with difficulties. In this paper, we concentrate on two aspects which are of importance when we seek to handle causality from an ontological point of view, The first concerns the range of particulars between which causal and causal-like relations may hold. In addition to events — the domain most typically chosen as the objects of causation — we consider the role played by processes and states, taking a particular view of the nature of these entities. The second aspect concerns the range of different causal and causal-like relations to be considered. In addition to causation itself we consider such things as initiation and termination, perpetuation, enablement and prevention. We do not present a fully-fledged ontological theory of causation, but lay down some basic ingredients that should be taken into account in the construction of such a theor

The Ontology of States, Processes, and Events

This paper presents a new view of the relationship between states, processes and events. Instead of trying to treat them as entities all on a similar footing, as most previous authors have done, we regard processes as abstract patterns of behaviour which may be realised in concrete form as actually occurring states or events. Processes are divided into two broad types, called continuables and repeatables, and various mappings between and within these categories are considered. The theory presented here is consistent with recent theorising about processes in ontology and computer science while being sensitive to the insights from the work of philosophers and linguists over many years

Ontological Levels in Histological Imaging

Paper presented at the 9th edition of the Formal Ontology in Information Systems conference, FOIS 2016, July 6–9, 2016, Annecy, FranceThis is the author accepted manuscript. The final version is available from IOS Press via the DOI in this record.In this paper we present an ontological perspective on ongoing work in histological and histopathological imaging involving the quantitative and algorithmic analysis of digitised images of cells and tissues. We present the derivation of consistent histological models from initially captured images of prepared tissue samples as a progression through a number of ontological levels, each populated by its distinctive classes of entities related in systematic ways to entities at other levels. We see this work as contributing to ongoing efforts to provide a consistent and widely accepted suite of ontological resources such as those currently constituting the OBO Foundry, and where possible we draw links between our work and existing ontologies within that suite.This research is supported by EPSRC through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy”

Automatic thresholding from the gradients of region boundaries

We present an approach for automatic threshold segmentation of greyscale images. The procedure is inspired by a reinterpretation of the strategy observed in human operators when adjusting thresholds manually and interactively by means of ‘slider’ controls. The approach translates into two methods. The first one is suitable for single or multiple global thresholds to be applied globally to images and consists of searching for a threshold value that generates a phase whose boundary coincides with the largest gradients in the original image. The second method is a variation, implemented to operate on the discrete connected components of the thresholded phase (i.e. the binary regions) independently. Consequently, this becomes an adaptive local threshold procedure, which operates relative to regions, rather than to local image subsets as is the case in most local thresholding methods previously published. Adding constraints for specifying certain classes of expected objects in the images can improve the output of the method over the traditional ‘segmenting first, then classify’ approach.The research reported in this paper was supported by the Engineering and Physical Sciences Research Council (EPSRC), UK through funding under grant EP/M023869/1 ‘Novel contextbased segmentation algorithms for intelligent microscopy’

Dermatoglyphic patterns in children with chronic constipation

Analysis of the fine ridge configurations on the digits of the palms and soles (dermatoglyphics) may sometimes help in the diagnoses of certain medical disorders. Dermatoglyphic patterns have been reported to be associated with congenital anomalies, such as congenital heart disease, duodenal ulcer, abdominal pain, and constipation. The palmar dermatoglyphic patterns of 77 children with constipation (39 functional and 38 organic constipation) were recorded. The control group consisted of 84 children with inguinal hernia. Those patients with at least one arch identified on any digit of either hand were termed arch positive. There was no significant correlation between arch positivity and constipation (functional or organic), or inguinal hernia (chi square, P =0.9211). Therefore, the presence of palmar arches cannot be used as a screening device for children with chronic constipation, especially of organic etiology.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44424/1/10620_2005_Article_BF02285186.pd

Best practice for motor imagery: a systematic literature review on motor imagery training elements in five different disciplines

<p>Abstract</p> <p>Background</p> <p>The literature suggests a beneficial effect of motor imagery (MI) if combined with physical practice, but detailed descriptions of MI training session (MITS) elements and temporal parameters are lacking. The aim of this review was to identify the characteristics of a successful MITS and compare these for different disciplines, MI session types, task focus, age, gender and MI modification during intervention.</p> <p>Methods</p> <p>An extended systematic literature search using 24 databases was performed for five disciplines: Education, Medicine, Music, Psychology and Sports. References that described an MI intervention that focused on motor skills, performance or strength improvement were included. Information describing 17 MITS elements was extracted based on the PETTLEP (physical, environment, timing, task, learning, emotion, perspective) approach. Seven elements describing the MITS temporal parameters were calculated: study duration, intervention duration, MITS duration, total MITS count, MITS per week, MI trials per MITS and total MI training time.</p> <p>Results</p> <p>Both independent reviewers found 96% congruity, which was tested on a random sample of 20% of all references. After selection, 133 studies reporting 141 MI interventions were included. The locations of the MITS and position of the participants during MI were task-specific. Participants received acoustic detailed MI instructions, which were mostly standardised and live. During MI practice, participants kept their eyes closed. MI training was performed from an internal perspective with a kinaesthetic mode. Changes in MI content, duration and dosage were reported in 31 MI interventions. Familiarisation sessions before the start of the MI intervention were mentioned in 17 reports. MI interventions focused with decreasing relevance on motor-, cognitive- and strength-focused tasks. Average study intervention lasted 34 days, with participants practicing MI on average three times per week for 17 minutes, with 34 MI trials. Average total MI time was 178 minutes including 13 MITS. Reporting rate varied between 25.5% and 95.5%.</p> <p>Conclusions</p> <p>MITS elements of successful interventions were individual, supervised and non-directed sessions, added after physical practice. Successful design characteristics were dominant in the Psychology literature, in interventions focusing on motor and strength-related tasks, in interventions with participants aged 20 to 29 years old, and in MI interventions including participants of both genders. Systematic searching of the MI literature was constrained by the lack of a defined MeSH term.</p

Unsupervised morphological segmentation of tissue compartments in histopathological images

Algorithmic segmentation of histologically relevant regions of tissues in digitized histopathological images is a critical step towards computer-assisted diagnosis and analysis. For example, automatic identification of epithelial and stromal tissues in images is important for spatial localisation and guidance in the analysis and characterisation of tumour micro-environment. Current segmentation approaches are based on supervised methods, which require extensive training data from high quality, manually annotated images. This is often difficult and costly to obtain. This paper presents an alternative data-independent framework based on unsupervised segmentation of oropharyngeal cancer tissue micro-arrays (TMAs). An automated segmentation algorithm based on mathematical morphology is first applied to light microscopy images stained with haematoxylin and eosin. This partitions the image into multiple binary ‘virtual-cells’, each enclosing a potential ‘nucleus’ (dark basins in the haematoxylin absorbance image). Colour and morphology measurements obtained from these virtual-cells as well as their enclosed nuclei are input into an advanced unsupervised learning model for the identification of epithelium and stromal tissues. Here we exploit two Consensus Clustering (CC) algorithms for the unsupervised recognition of tissue compartments, that consider the consensual opinion of a group of individual clustering algorithms. Unlike most unsupervised segmentation analyses, which depend on a single clustering method, the CC learning models allow for more robust and stable detection of tissue regions. The proposed framework performance has been evaluated on fifty-five hand-annotated tissue images of oropharyngeal tissues. Qualitative and quantitative results of the proposed segmentation algorithm compare favourably with eight popular tissue segmentation strategies. Furthermore, the unsupervised results obtained here outperform those obtained with individual clustering algorithms