Domain wall tilting in the presence of the Dzyaloshinskii-Moriya interaction in out-of-plane magnetized magnetic nanotracks

We show that the Dzyaloshinskii-Moriya interaction (DMI) can lead to a tilting of the domain wall (DW) surface in perpendicularly magnetized magnetic nanotracks when DW dynamics is driven by an easy axis magnetic field or a spin polarized current. The DW tilting affects the DW dynamics for large DMI and the tilting relaxation time can be very large as it scales with the square of the track width. The results are well explained by an analytical model based on a Lagrangian approach where the DMI and the DW tilting are included. We propose a simple way to estimate the DMI in a magnetic multilayers by measuring the dependence of the DW tilt angle on a transverse static magnetic field. Our results shed light on the current induced DW tilting observed recently in Co/Ni multilayers with inversion asymmetry, and further support the presence of DMI in these systems.Comment: 12 pages, 3 figures, 1 Supplementary Material

Evaluation of the single platform Muse® Auto CD4/CD4 % system in Cameroon

Background: according to who revised guidelines for scaling up antiretroviral therapy (ART) in adults and children living in resource-limited settings, there is an urgent need for laboratory monitoring, including the numeration of CD4 T cells.Objective: the study compared the muse® auto CD4/CD4% System for CD4 t cell enumeration in absolute counts and in percentages, to the Guava® AutoD4/CD4% System.Design: This was a prospective study using adults, adolescents, children and infant’s samples.Setting: The Centre International de Diagnostic Medical (CIDM), Yaounde, a research laboratory devoted to HIV screening and monitoring affiliated to the University of Yaounde I.Subjects: K3-EDTA-blood samples from 111 patients (77 adults, 12 adolescents, 18 children and 4 infants) were collected and tested. All participants signed an informed consent form whereas the guardian and parent of children signed the assent form.Results: the absolute CD4 t lymphocyte counts as well as the percentage CD4 lymphocyte of the Muse® AutoCD4/CD4% and GuavaAutoCD4/CD4% Systems, were highly correlated with an interclass correlation coefficient of 0.997 (95%CI: 0.996-0.998) and 0.991 (95% CI: 0.987-0.994) respectively. The Bland-Altman analysis limits of agreement were -5.79 cells/μl (95%CI: [-97.77; 86.19]) for the absolute CD4 T lymphocyte counts and -1.93 (95%CI: [-7.29; – 3.43]) for CD4 T lymphocyte percentage. The numbers of outliers were similar (6/111=5.41%) both for CD4 T lymphocyte counts and percentage. In addition, Cohen’s Kappa ranged from 0.95 to 1 according to CD4 T lymphocyte counts thresholds (p<0.001), showing agreement between both methods. Conclusion: this study demonstrates that the muse™ auto CD4/CD4% system constitutes a promising system for CD4 t cell counting comparable to existing reference methods, and should facilitate wider access to CD4 T cell enumeration for adults and children with HIV infection living in resource-limited countries

Increased Population Prevalence of Low Pertussis Toxin Antibody Levels in Young Children Preceding a Record Pertussis Epidemic in Australia

Background: Cross-sectional serosurveys using IgG antibody to pertussis toxin (IgG-PT) are increasingly being used to estimate trends in recent infection independent of reporting biases. Methods/Principal Findings: We compared the age-specific seroprevalence of various levels of IgG-PT in cross-sectional surveys using systematic collections of residual sera from Australian diagnostic laboratories in 1997/8, 2002 and 2007 with reference to both changes in the pertussis vaccine schedule and the epidemic cycle, as measured by disease notifications. A progressive decline in high-level ($62.5 EU/ml) IgG-PT prevalence from 19 % (95 % CI 16–22%) in 1997/98 to 12 % (95 % CI 11–14%) in 2002 and 5 % (95 % CI 4–6%) in 2007 was consistent with patterns of pertussis notifications in the year prior to each collection. Concomitantly, the overall prevalence of undetectable (,5 EU/ml) levels increased from 17 % (95 % CI 14– 20%) in 1997/98 to 38 % (95 % CI 36–40%) in 2007 but among children aged 1–4 years, from 25 % (95 % CI 17–34%) in 1997/98 to 62 % (95 % CI 56–68%) in 2007. This change followed withdrawal of the 18-month booster dose in 2003 and preceded record pertussis notifications from 2008 onwards. Conclusions/Significance: Population seroprevalence of high levels of IgG-PT is accepted as a reliable indicator of pertussis disease activity over time within and between countries with varying diagnostic practices, especially in unimmunised age groups. Our novel findings suggest that increased prevalence of undetectable IgG-PT is an indicator of waning immunit

A recommender system for process discovery

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.Peer ReviewedPostprint (author’s final draft

Re-viewing lace in archives: connecting the lacunae

The archive is widely understood to be an ordered keeper of factual truth and a solid foundation of historical accuracy. However, the inherent lacunae within the archive can render this assumed accuracy fallible. This thesis questions the potential of such gaps and absences to impact on the understanding of objects in archives. An archive is defined as any collection of material which has been withdrawn from its normal circulation and stored for potential future reference

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol

Introduction In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications or finance, a methodology known as ‘process mining’ has become an established and successful method to identify how an organisation can best deploy resources to meet the needs of its clients and customers. Here and for the first time in the UK, we will apply it to primary care settings to gain a greater understanding of how patients with two of the most common chronic conditions are managed. Methods and analysis The study will be conducted in three phases; first, we will apply process mining algorithms to the data held on the clinical management system of four practices of varying characteristics in the West Midlands to determine how each interacts with patients with hypertension or type 2 diabetes. Second, we will use traditional process mapping exercises at each practice to manually produce maps of care processes for the selected condition. Third, with the aid of staff and patients at each practice, we will compare and contrast the process models produced by process mining with the process maps produced via manual techniques, review differences and similarities between them and the relative importance of each. The first pilot study will be on hypertension and the second for patients diagnosed with type 2 diabetes

Extracting event data from databases to unleash process mining

Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards "better" processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models "raw data" need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create "flat" event logs that can be analyzed using traditional process-mining techniques

User-guided discovery of declarative process models

Process mining techniques can be used to effectively discover process models from logs with example behaviour. Cross-correlating a discovered model with information in the log can be used to improve the underlying process. However, existing process discovery techniques have two important drawbacks. The produced models tend to be large and complex, especially in flexible environments where process executions involve multiple alternatives. This "overload" of information is caused by the fact that traditional discovery techniques construct procedural models explicitly showing all possible behaviours. Moreover, existing techniques offer limited possibilities to guide the mining process towards specific properties of interest. These problems can be solved by discovering declarative models. Using a declarative model, the discovered process behaviour is described as a (compact) set of rules. Moreover, the discovery of such models can easily be guided in terms of rule templates. This paper uses DECLARE, a declarative language that provides more flexibility than conventional procedural notations such as BPMN, Petri nets, UML ADs, EPCs and BPEL. We present an approach to automatically discover DECLARE models. This has been implemented in the process mining tool ProM. Our approach and toolset have been applied to a case study provided by the company Thales in the domain of maritime safety and security

Extracting event data from databases to unleash process mining

Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards "better" processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models "raw data" need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create "flat" event logs that can be analyzed using traditional process-mining techniques