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

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

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

Association of Attitudes and Beliefs towards Antiretroviral Therapy with HIV-Seroprevalence in the General Population of Kisumu, Kenya

Since antiretroviral therapy (ART) became available in the developed world, the prevalence of unprotected sex and the incidence of sexually transmitted infections (STIs) and HIV have increased. We hypothesized that a similar phenomenon may be occurring in sub-Saharan Africa concomitant with the scale-up of HIV treatment.We conducted a general population-based survey in Kisumu, Kenya. Participants completed an interview that included demographics as well as ART-related attitudes and beliefs (AB) and then underwent HIV serological testing. Exploratory and confirmatory factor analyses of AB about ART indicated two factors: 1) ART-related risk compensation (increased sexual risk taking now that ART is available); and 2) a perception that HIV is more controllable now that ART is available. Logistic regression was used to determine associations of these factors with HIV-seroprevalence after controlling for age.1,655 (90%) of 1,844 people aged 15-49 contacted, including 749 men and 906 women, consented to participate in the study. Most participants (n = 1164; 71%) had heard of ART. Of those who had heard of ART, 23% believed ART was a cure for HIV. ART-related risk compensation (Adjusted (A)OR = 1.45, 95% CI 1.16-1.81), and a belief that ART cures HIV (AOR = 2.14, 95% CI 1.22-3.76) were associated with an increased HIV seroprevalence in men but not women after controlling for age. In particular, ART-related risk compensation was associated with an increased HIV-seroprevalence in young (aged 15-24 years) men (OR = 1.56; 95% CI 1.12-2.19).ART-related risk compensation and a belief that ART cures HIV were associated with an increased HIV seroprevalence among men but not women. HIV prevention programs in sub-Saharan Africa that target the general population should include educational messages about ART and address the changing beliefs about HIV in the era of greater ART availability

Polymerase delta-interacting protein 38 (PDIP38) modulates the stability and activity of the mitochondrial AAA+ protease CLPXP

Over a decade ago Polymerase δ interacting protein of 38 kDa (PDIP38) was proposed to play a role in DNA repair. Since this time, both the physiological function and subcellular location of PDIP38 has remained ambiguous and our present understanding of PDIP38 function has been hampered by a lack of detailed biochemical and structural studies. Here we show, that human PDIP38 is directed to the mitochondrion in a membrane potential dependent manner, where it resides in the matrix compartment, together with its partner protein CLPX. Our structural analysis revealed that PDIP38 is composed of two conserved domains separated by an α/β linker region. The N-terminal (YccV-like) domain of PDIP38 forms an SH3-like β-barrel, which interacts specifically with CLPX, via the adaptor docking loop within the N-terminal Zinc binding domain of CLPX. In contrast, the C-terminal (DUF525) domain forms an immunoglobin-like β-sandwich fold, which contains a highly conserved putative substrate binding pocket. Importantly, PDIP38 modulates the substrate specificity of CLPX and protects CLPX from LONM-mediated degradation, which stabilises the cellular levels of CLPX. Collectively, our findings shed new light on the mechanism and function of mitochondrial PDIP38, demonstrating that PDIP38 is a bona fide adaptor protein for the mitochondrial protease, CLPXP