The consistency of extinction risk classification protocols

Systematic protocols that use decision rules or scores arc, seen to improve consistency and transparency in classifying the conservation status of species. When applying these protocols, assessors are typically required to decide on estimates for attributes That are inherently uncertain, Input data and resulting classifications are usually treated as though they arc, exact and hence without operator error We investigated the impact of data interpretation on the consistency of protocols of extinction risk classifications and diagnosed causes of discrepancies when they occurred. We tested three widely used systematic classification protocols employed by the World Conservation Union, NatureServe, and the Florida Fish and Wildlife Conservation Commission. We provided 18 assessors with identical information for 13 different species to infer estimates for each of the required parameters for the three protocols. The threat classification of several of the species varied from low risk to high risk, depending on who did the assessment. This occurred across the three Protocols investigated. Assessors tended to agree on their placement of species in the highest (50-70%) and lowest risk categories (20-40%), but There was poor agreement on which species should be placed in the intermediate categories, Furthermore, the correspondence between The three classification methods was unpredictable, with large variation among assessors. These results highlight the importance of peer review and consensus among multiple assessors in species classifications and the need to be cautious with assessments carried out 4), a single assessor Greater consistency among assessors requires wide use of training manuals and formal methods for estimating parameters that allow uncertainties to be represented, carried through chains of calculations, and reported transparently.

Examination of the predictive value of structural magnetic resonance scans in bipolar disorder:a pattern classification approach

Background - Bipolar disorder (BD) is one of the leading causes of disability worldwide. Patients are further disadvantaged by delays in accurate diagnosis ranging between 5 and 10 years. We applied Gaussian process classifiers (GPCs) to structural magnetic resonance imaging (sMRI) data to evaluate the feasibility of using pattern recognition techniques for the diagnostic classification of patients with BD. Method - GPCs were applied to gray (GM) and white matter (WM) sMRI data derived from two independent samples of patients with BD (cohort 1: n = 26; cohort 2: n = 14). Within each cohort patients were matched on age, sex and IQ to an equal number of healthy controls. Results - The diagnostic accuracy of the GPC for GM was 73% in cohort 1 and 72% in cohort 2; the sensitivity and specificity of the GM classification were respectively 69% and 77% in cohort 1 and 64% and 99% in cohort 2. The diagnostic accuracy of the GPC for WM was 69% in cohort 1 and 78% in cohort 2; the sensitivity and specificity of the WM classification were both 69% in cohort 1 and 71% and 86% respectively in cohort 2. In both samples, GM and WM clusters discriminating between patients and controls were localized within cortical and subcortical structures implicated in BD. Conclusions - Our results demonstrate the predictive value of neuroanatomical data in discriminating patients with BD from healthy individuals. The overlap between discriminative networks and regions implicated in the pathophysiology of BD supports the biological plausibility of the classifiers.

Shared action spaces:a basis function framework for social re-calibration of sensorimotor representations supporting joint action

The article explores the possibilities of formalizing and explaining the mechanisms that support spatial and social perspective alignment sustained over the duration of a social interaction. The basic proposed principle is that in social contexts the mechanisms for sensorimotor transformations and multisensory integration (learn to) incorporate information relative to the other actor(s), similar to the "re-calibration" of visual receptive fields in response to repeated tool use. This process aligns or merges the co-actors' spatial representations and creates a "Shared Action Space" (SAS) supporting key computations of social interactions and joint actions; for example, the remapping between the coordinate systems and frames of reference of the co-actors, including perspective taking, the sensorimotor transformations required for lifting jointly an object, and the predictions of the sensory effects of such joint action. The social re-calibration is proposed to be based on common basis function maps (BFMs) and could constitute an optimal solution to sensorimotor transformation and multisensory integration in joint action or more in general social interaction contexts. However, certain situations such as discrepant postural and viewpoint alignment and associated differences in perspectives between the co-actors could constrain the process quite differently. We discuss how alignment is achieved in the first place, and how it is maintained over time, providing a taxonomy of various forms and mechanisms of space alignment and overlap based, for instance, on automaticity vs. control of the transformations between the two agents. Finally, we discuss the link between low-level mechanisms for the sharing of space and high-level mechanisms for the sharing of cognitive representations. © 2013 Pezzulo, Iodice, Ferraina and Kessler.

On the Recursive Estimation of the Location and of the Size of the Mode of a Probability Density

2000 Mathematics Subject Classification: 62G07, 62L20.

Serum procalcitonin, interleukin-6, soluble intercellular adhesin molecule-1 and IgG to short-chain exocellular lipoteichoic acid as predictors of infection in total joint prosthesis revision

The diagnosis of prosthetic joint infection and its differentiation from aseptic loosening remains problematic. The definitive laboratory diagnostic test is the recovery of identical infectious agents from multiple intraoperative tissue samples; however, interpretation of positive cultures is often complex as infection is frequently associated with low numbers of commensal microorganisms, in particular the coagulase-negative staphylococci (CNS). In this investigation, the value of serum procalcitonin (PCT), interleukin-6 (IL-6) and soluble intercellular adhesion molecule-1 (sICAM-1) as predictors of infection in revision hip replacement surgery is assessed. Furthermore, the diagnostic value of serum IgG to short-chain exocellular lipoteichoic acid (sce-LTA) is assessed in patients with infection due to CNS. Presurgical levels of conventional serum markers of infection including C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and white blood cell count (WBC) is also established. Forty-six patients undergoing revision hip surgery were recruited with a presumptive clinical diagnosis of either septic (16 patients) or aseptic loosening (30 patients). The diagnosis was confirmed microbiologically and levels of serum markers were determined. Serum levels of IL-6 and sICAM-1 were significantly raised in patients with septic loosening (P=0.001 and P=0.0002, respectively). Serum IgG to sce-LTA was elevated in three out of four patients with infection due to CNS. In contrast, PCT was not found to be of value in differentiating septic and aseptic loosening. Furthermore, CRP, ESR and WBC were significantly higher (P=0.0001, P=0.0001 and P=0.003, respectively) in patients with septic loosening. Serum levels of IL-6, sICAM-1 and IgG to sce-LTA may provide additional information to facilitate the diagnosis of prosthetic joint infection.

Multidimensional classification and diagnosis of leukemia in flow cytometry

This research is to establish new optimization methods for pattern recognition and classification of different white blood cells in actual patient data to enhance the process of diagnosis. Beckman-Coulter Corporation supplied flow cytometry data of numerous patients that are used as training sets to exploit the different physiological characteristics of the different samples provided. The methods of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used as promising pattern classification techniques to identify different white blood cell samples and provide information to medical doctors in the form of diagnostic references for the specific disease states, leukemia. The obtained results prove that when a neural network classifier is well configured and trained with cross-validation, it can perform better than support vector classifiers alone for this type of data. Furthermore, a new unsupervised learning algorithm---Density based Adaptive Window Clustering algorithm (DAWC) was designed to process large volumes of data for finding location of high data cluster in real-time. It reduces the computational load to ∼O(N) number of computations, and thus making the algorithm more attractive and faster than current hierarchical algorithms.

Hyperspectral Image Classification with Nonlinear Methods

This thesis introduces two related lines of study on classification of hyperspectral images with nonlinear methods. First, it describes a quantitative and systematic evaluation, by the author, of each major component in a pipeline for classifying hyperspectral images (HSI) developed earlier in a joint collaboration [23]. The pipeline, with novel use of nonlinear classification methods, has reached beyond the state of the art in classification accuracy on commonly used benchmarking HSI data [6], [13]. More importantly, it provides a clutter map, with respect to a predetermined set of classes, toward the real application situations where the image pixels not necessarily fall into a predetermined set of classes to be identified, detected or classified with.

The particular components evaluated are a) band selection with band-wise entropy spread, b) feature transformation with spatial filters and spectral expansion with derivatives c) graph spectral transformation via locally linear embedding for dimension reduction, and d) statistical ensemble for clutter detection. The quantitative evaluation of the pipeline verifies that these components are indispensable to high-accuracy classification.

Secondly, the work extends the HSI classification pipeline with a single HSI data cube to multiple HSI data cubes. Each cube, with feature variation, is to be classified of multiple classes. The main challenge is deriving the cube-wise classification from pixel-wise classification. The thesis presents the initial attempt to circumvent it, and discuss the potential for further improvement.

Characteristics of samples obtained during Pokhodsk 2012-2013 campaigns in the joint Russian-German POLYGON Project

Polygonal tundra, thermokarst basins and pingos are common and characteristic periglacial features of arctic lowlands underlain by permafrost in Northeast Siberia. Modern polygonal mires are in the focus of biogeochemical, biological, pedological, and cryolithological research with special attention to their carbon stocks and greenhouse-gas fluxes, their biodiversity and their dynamics and functioning under past, present and future climate scenarios. Within the frame of the joint German-Russian DFG-RFBR project Polygons in tundra wetlands: state and dynamics under climate variability in Polar Regions (POLYGON) field studies of recent and of late Quaternary environmental dynamics were carried out in the Indigirka lowland and in the Kolyma River Delta in summer 2012 and summer 2013. Using a multidisciplinary approach, several types of polygons and thermokarst lakes were studied in different landscapes units in the Kolyma Delta in 2012 around the small fishing settlement Pokhodsk. The floral and faunal associations of polygonal tundra were described during the fieldwork. Ecological, hydrological, meteorological, limnological, pedological and cryological features were studied in order to evaluate modern and past environmental conditions and their essential controlling parameters. The ecological monitoring and collection program of polygonal ponds were undertaken as in 2011 in the Indigirka lowland by a former POLYGON expedition (Schirrmeister et al. [eds.] 2012). Exposures, pits and drill cores in the Kolyma Delta were studied to understand the cryolithological structures of frozen ground and to collect samples for detailed paleoenvironmental research of the late Quaternary past. Dendrochronological and ecological studies were carried out in the tree line zone south of the Kolyma Delta. Based on previous work in the Indigirka lowland in 2011 (Schirrmeister et al. [eds.] 2012), the environmental monitoring around the Kytalyk research station was continued until the end of August 2012. In addition, a classical exposure of the late Pleistocene permafrost at the Achchaygy Allaikha River near Chokurdakh was studied. The ecological studies near Pokhodsk were continued in 2013 (chapter 13). Other fieldwork took place at the Pokhodsk-Yedoma-Island in the northwestern part of the Kolyma Delta.

Scaling up strategies of the chronic respiratory disease programme of the European Innovation Partnership on Active and Healthy Ageing (Action Plan B3: Area 5)

Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) focuses on the integrated care of chronic diseases. Area 5 (Care Pathways) was initiated using chronic respiratory diseases as a model. The chronic respiratory disease action plan includes (1) AIRWAYS integrated care pathways (ICPs), (2) the joint initiative between the Reference site MACVIA-LR (Contre les MAladies Chroniques pour un VIeillissement Actif) and ARIA (Allergic Rhinitis and its Impact on Asthma), (3) Commitments for Action to the European Innovation Partnership on Active and Healthy Ageing and the AIRWAYS ICPs network. It is deployed in collaboration with the World Health Organization Global Alliance against Chronic Respiratory Diseases (GARD). The European Innovation Partnership on Active and Healthy Ageing has proposed a 5-step framework for developing an individual scaling up strategy: (1) what to scale up: (1-a) databases of good practices, (1-b) assessment of viability of the scaling up of good practices, (1-c) classification of good practices for local replication and (2) how to scale up: (2-a) facilitating partnerships for scaling up, (2-b) implementation of key success factors and lessons learnt, including emerging technologies for individualised and predictive medicine. This strategy has already been applied to the chronic respiratory disease action plan of the European Innovation Partnership on Active and Healthy Ageing.

Outlier effects on robust joint modelling of longitudinal and survival data

Robust joint modelling is an emerging field of research. Through the advancements in electronic patient healthcare records, the popularly of joint modelling approaches has grown rapidly in recent years providing simultaneous analysis of longitudinal and survival data. This research advances previous work through the development of a novel robust joint modelling methodology for one of the most common types of standard joint models, that which links a linear mixed model with a Cox proportional hazards model. Through t-distributional assumptions, longitudinal outliers are accommodated with their detrimental impact being down weighed and thus providing more efficient and reliable estimates. The robust joint modelling technique and its major benefits are showcased through the analysis of Northern Irish end stage renal disease patients. With an ageing population and growing prevalence of chronic kidney disease within the United Kingdom, there is a pressing demand to investigate the detrimental relationship between the changing haemoglobin levels of haemodialysis patients and their survival. As outliers within the NI renal data were found to have significantly worse survival, identification of outlying individuals through robust joint modelling may aid nephrologists to improve patient's survival. A simulation study was also undertaken to explore the difference between robust and standard joint models in the presence of increasing proportions and extremity of longitudinal outliers. More efficient and reliable estimates were obtained by robust joint models with increasing contrast between the robust and standard joint models when a greater proportion of more extreme outliers are present. Through illustration of the gains in efficiency and reliability of parameters when outliers exist, the potential of robust joint modelling is evident. The research presented in this thesis highlights the benefits and stresses the need to utilise a more robust approach to joint modelling in the presence of longitudinal outliers.

Study of a beam-column joint of a precast system by nonlinear numerical analysis

The present work consists of a detailed numerical analysis of a 4-way joint made of a precast column and two partially precast beams. The structure has been previously built and experimentally analyzed through a series of cyclic loads at the Laboratory of Tests on Structures (Laboratorio di Prove su Strutture, La. P. S.) of the University of Bologna. The aim of this work is to design a 3D model of the joint and then apply the techniques of nonlinear finite element analysis (FEA) to computationally reproduce the behavior of the structure under cyclic loads. Once the model has been calibrated to correctly emulate the joint, it is possible to obtain new insights useful to understand and explain the physical phenomena observed in the laboratory and to describe the properties of the structure, such as the cracking patterns, the force-displacement and the moment-curvature relations, as well as the deformations and displacements of the various elements composing the joint.

Multi-physics modelling - A vital component of virtual manufacturing

One of the core tasks of the virtual-manufacturing environment is to characterise the transformation of the state of material during each of the unit processes. This transformation in shape, material properties, etc. can only be reliably achieved through the use of models in a simulation context. Unfortunately, many manufacturing processes involve the material being treated in both the liquid and solid state, the trans-formation of which may be achieved by heat transfer and/or electro-magnetic fields. The computational modelling of such processes, involving the interactions amongst various interacting phenomena, is a consider-able challenge. However, it must be addressed effectively if Virtual Manufacturing Environments are to become a reality! This contribution focuses upon one attempt to develop such a multi-physics computational toolkit. The approach uses a single discretisation procedure and provides for direct interaction amongst the component phenomena. The need to exploit parallel high performance hardware is addressed so that simulation elapsed times can be brought within the realms of practicality. Examples of Multiphysics modelling in relation to shape casting, and solder joint formation reinforce the motivation for this work.

Scaling up strategies of the chronic respiratory disease programme of the European Innovation Partnership on Active and Healthy Ageing (Action Plan B3: Area 5)

Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) focuses on the integrated care of chronic diseases. Area 5 (Care Pathways) was initiated using chronic respiratory diseases as a model. The chronic respiratory disease action plan includes (1) AIRWAYS integrated care pathways (ICPs), (2) the joint initiative between the Reference site MACVIA-LR (Contre les MAladies Chroniques pour un VIeillissement Actif) and ARIA (Allergic Rhinitis and its Impact on Asthma), (3) Commitments for Action to the European Innovation Partnership on Active and Healthy Ageing and the AIRWAYS ICPs network. It is deployed in collaboration with the World Health Organization Global Alliance against Chronic Respiratory Diseases (GARD). The European Innovation Partnership on Active and Healthy Ageing has proposed a 5-step framework for developing an individual scaling up strategy: (1) what to scale up: (1-a) databases of good practices, (1-b) assessment of viability of the scaling up of good practices, (1-c) classification of good practices for local replication and (2) how to scale up: (2-a) facilitating partnerships for scaling up, (2-b) implementation of key success factors and lessons learnt, including emerging technologies for individualised and predictive medicine. This strategy has already been applied to the chronic respiratory disease action plan of the European Innovation Partnership on Active and Healthy Ageing.