Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibility-weighted imaging (SWI): initial results

Objectives: To diagnose Parkinson disease (PD) at the individual level using pattern recognition of brain susceptibility-weighted imaging (SWI). Methods: We analysed brain SWI in 36 consecutive patients with Parkinsonism suggestive of PD who had (1) SWI at 3T, (2) brain 123I-ioflupane SPECT and (3) extensive neurological testing including follow-up (16 PD, 67.4 ± 6.2years, 11 female; 20 OTHER, a heterogeneous group of atypical Parkinsonism syndromes 65.2 ± 12.5years, 6 female). Analysis included group-level comparison of SWI values and individual-level support vector machine (SVM) analysis. Results: At the group level, simple visual analysis yielded no differences between groups. However, the group-level analyses demonstrated increased SWI in the bilateral thalamus and left substantia nigra in PD patients versus other Parkinsonism. The inverse comparison yielded no supra-threshold clusters. At the individual level, SVM correctly classified PD patients with an accuracy above 86%. Conclusions: SVM pattern recognition of SWI data provides accurate discrimination of PD among patients with various forms of Parkinsonism at an individual level, despite the absence of visually detectable alterations. This pilot study warrants further confirmation in a larger cohort of PD patients and with different MR machines and MR parameters. Key Points: • Magnetic resonance imaging data offers new insights into Parkinson's disease • Visual susceptibility-weighted imaging (SWI) analysis could not discriminate idiopathic from atypical PD • However, support vector machine (SVM) analysis provided highly accurate detection of idiopathic PD • SVM analysis may contribute to the clinical diagnosis of individual PD patients • Such information can be readily obtained from routine MR dat

Personalised trails and learner profiling in an e-learning environment

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Long-lasting stem cell-like memory CD8+ T cells with a naïve-like profile upon yellow fever vaccination.

Efficient and persisting immune memory is essential for long-term protection from infectious and malignant diseases. The yellow fever (YF) vaccine is a live attenuated virus that mediates lifelong protection, with recent studies showing that the CD8(+) T cell response is particularly robust. Yet, limited data exist regarding the long-term CD8(+) T cell response, with no studies beyond 5 years after vaccination. We investigated 41 vaccinees, spanning 0.27 to 35 years after vaccination. YF-specific CD8(+) T cells were readily detected in almost all donors (38 of 41), with frequencies decreasing with time. As previously described, effector cells dominated the response early after vaccination. We detected a population of naïve-like YF-specific CD8(+) T cells that was stably maintained for more than 25 years and was capable of self-renewal ex vivo. In-depth analyses of markers and genome-wide mRNA profiling showed that naïve-like YF-specific CD8(+) T cells in vaccinees (i) were distinct from genuine naïve cells in unvaccinated donors, (ii) resembled the recently described stem cell-like memory subset (Tscm), and (iii) among all differentiated subsets, had profiles closest to naïve cells. Our findings reveal that CD8(+) Tscm are efficiently induced by a vaccine in humans, persist for decades, and preserve a naïveness-like profile. These data support YF vaccination as an optimal mechanistic model for the study of long-lasting memory CD8(+) T cells in humans

The Water Bugs (Heteroptera: Nepomorpha) of the Guyana Region

NEPOMORPHA OF THE GUYANA REGION The Nepomorpha of the Guyana Region are keyed out and described. In addition distributional, faunistical and comparative notes on the species are given. New species and subspecies: Ochterus aeneifrons surinamensis, O. tenebrosus; Limnocoris fittkaui surinamensis; Ranatra adelomorpha; Neoplea globoidea; Buenoa amnigenopsis; Tenagobia pseudoromani from Suriname and Ranatra ornitheia from Guyana. New synonyms (junior ones between parenthesis): Gelaslocorus flavus flavus Guér. (G. nebulosus nebulosus Guér.); Pelocoris impicticollis Stål (P. horváthi Mont.), P. poeyi (Guér.) not identical with P. femoratus (P.-B.) (P. convexus Nieser), P. procurrens White (P. minutus Mont.); Belostoma bicavum Lauck ( B. parvoculum Lauck); Ranatra doesburgi De Carlo (R. usingeri De C.), R. macrophthalma H.-S. (R. surinamensis De C.), R. mediana Mont. (R. williamsi Kuitert), R. obscura Mont. (R. annulipes White 1879 not Stål), R. sarmentoi De C. (R. ameghinoi De C.); Buenoa amnigenopsis n. sp. ( B. amnigenus Nieser 1968, 1970 not White), B. amnigenus (White) (B. amnigenoidea Nieser 1970), B. nitida Truxal (B. doesburgi Nieser); Heterocorixa surinamensis Nieser ( H. boliviensis Nieser 1970 not Hungerford); Tenagobia incerta Lundbl. ( T. signata and T. serrata in part, Nieser 1970 not White and Deay respectively), T. socialis (White) (T. serrata in part, Nieser 1970 not Deay)

Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation

Background: Differentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells. Results: We perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing. Conclusion: The link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

Introduction: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. Aim: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. Methods: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. Results: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. Conclusions: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources