Chapter Developing Active Citizenship Through Adult Learning and Education. Experiences from an INTALL Winter School Comparative Working Group

Active citizenship became a research issue for adult learning and education in 1995 when the Council of Ministers decided to make 1996 the Year of Lifelong Learning. Moreover, the Lisbon programme, in the year 2000, reinforced the relevance of the issue and, along with employability, connected it to lifelong learning. That is why since 2001 comparative adult learning and education researchers have put a specific focus on analysing active citizenship and bridging it to adult learning. For this very reason, a distinguished Comparative Working Group was formed at the 2019 Winter School of the Erasmus+ Intall project—on the one hand, to collect different national/regional and local narratives and understandings of active citizenship and, on the other, to gather examples, good practices, formations of active citizens, or trajectories of how to learn for active citizenship as routes and processes of lifelong learning. The same Winter School comparative group tried to analyse the similarities and differences collected in an effort to relate them to existing theoretical frames offered by key authors on the topic, including Baert, Jansen, Jarvis, Johnston, Wildemeeersch, and others. This paper discusses the experiences of the comparative working group and formulates some special conclusions and comments for further actions of comparative studies in adult learning and education

Ion camera development for real–time acquisition of localised pH responses using the CMOS based 64×64–pixel ISFET sensor array technology

This thesis presents the development and test of an integrated ion camera chip for monitoring highly localised ion fluxes of electrochemical processes using an ion sensitive sensor array. Ionic concentration fluctuations are shown to travel across the sensor array as a result of citric acid injection and the BZ-reaction. The imaging capability of non-equilibrium chemical activities is also demonstrated monitoring self-assembling micrometre sized polyoxometalate tubular and membranous architectures. The sufficient spatial resolution for the visualisation of the 10-60 µm wide growing trajectories is provided by the dense sensor array containing 64×64 pixels. In the case of citric acid injection and the BZ-reaction the ion camera chip is shown to be able to resolve pH differences with resolution as low as the area of one pixel. As a result of the transient and volatile ionic fluxes high time resolution is required, thus the signal capturing can be performed in real.time at the maximum sampling rate of 40 µs per pixel, 10.2 ms per array. The extracted sensor data are reconstructed into ionic images and thus the ionic activities can be displayed as individual figures as well as continuous video recordings. This chip is the first prototype in the envisioned establishment of a fully automated CMOS based ion camera system which would be able to image the invisible activity of ions using a single microchip. In addition the capability of detecting ultra-low level pH oscillations in the extracellular space is demonstrated using cells of the slime mould organism. The detected pH oscillations with extent of ~0.022 pH furthermore raise the potential for observing fluctuations of ion currents in cell based tissue environments. The intrinsic noise of the sensor devices are measured to observe noise effect on the detected low level signals. It is experimentally shown that the used ion sensitive circuits, similarly to CMOS, also demonstrate 1/f noise. In addition the reference bias and pH sensitivity of the measured noise is confirmed. Corresponding to the measurement results the noise contribution is approximated with a 28.2 µV peak-to-peak level and related to the 450 µV �+/- 70 µV peak-to-peak oscillations amplitudes of the slime mould. Thus a maximum intrinsic noise contribution of 6.2 �+/- 1.2 % is calculated. A H+ flickering hypothesis is also presented that correlates the pH fluctuations on the surface of the device with the intrinsic 1/f noise. The ion camera chip was fabricated in an unmodified 4-metal 0.35 µm CMOS process and the ionic imaging technology was based on a 64�×64-pixel ion sensitive field effect transistor (ISFET) array. The high-speed and synchronous operation of the 4096 ISFET sensors occupying 715.8×715.8 µm space provided a spatial resolution as low as one pixel. Each pixel contained 4 transistors with 10.2×10.2 µm layout dimensions and the pixels were separated by a 1 µm separation gap. The ion sensitive silicon nitride based passivation layer was in contact with the floating gates of the ISFET sensors. It allowed the capacitive measurements of localised changes in the ionic concentrations, e.g. pH, pNa, on the surface of the chip. The device showed an average ionic sensitivity of 20 mV/pH and 9 mV/pNa. The packaging and encapsulation was carried out using PGA-100 chip carriers and two-component epoxies. Custom designed printed circuit boards (PCBs) were used to provide interface between the ISFET array chip and the data acquisition system. The data acquisition and extraction part of the developed software system was based on LabVIEW, the data processing was carried out on Matlab platform

Measuring and filtering reactive inhibition is essential for assessing serial decision making and learning

Learning complex structures from stimuli requires extended exposure and often repeated observation of the same stimuli. Learning induces stimulus-dependent changes in specific performance measures. The same performance measures, however, can also be affected by processes that arise due to extended training (e.g. fatigue) but are otherwise independent from learning. Thus, a thorough assessment of the properties of learning can only be achieved by identifying and accounting for the effects of such processes. Reactive inhibition is a process that modulates behavioral performance measures on a wide range of time scales and often has opposite effects than learning. Here we develop a tool to disentangle the effects of reactive inhibition from learning in the context of an implicit learning task, the alternating serial reaction time task. Our method highlights that the magnitude of the effect of reactive inhibition on measured performance is larger than that of the acquisition of statistical structure from stimuli. We show that the effect of reactive inhibition can be identified not only in population measures but also at the level of performance of individuals, revealing varying degrees of contribution of reactive inhibition. Finally, we demonstrate that a higher proportion of behavioral variance can be explained by learning once the effects of reactive inhibition are eliminated. These results demonstrate that reactive inhibition has a fundamental effect on the behavioral performance that can be identified in individual participants and can be separated from other cognitive processes like learning

The common truncation variant in pancreatic lipase related protein 2 (PNLIPRP2) is expressed poorly and does not alter risk for chronic pancreatitis

A nonsense variant (p.W358X) of human pancreatic lipase related protein 2 (PNLIPRP2) is present in different ethnic populations with a high allele frequency. In cell culture experiments, the truncated protein mainly accumulates inside the cells and causes endoplasmic reticulum stress. Here, we tested the hypothesis that variant p.W358X might increase risk for chronic pancreatitis through acinar cell stress. We sequenced exon 11 of PNLIPRP2 in a cohort of 256 subjects with chronic pancreatitis (152 alcoholic and 104 non-alcoholic) and 200 controls of Hungarian origin. We observed no significant difference in the distribution of the truncation variant between patients and controls. We analyzed mRNA expression in human pancreatic cDNA samples and found the variant allele markedly reduced. We conclude that the p.W358X truncation variant of PNLIPRP2 is expressed poorly and has no significant effect on the risk of chronic pancreatitis

Cost-Utility Analysis of Heberprot-P as an Add-on Therapy to Good Wound Care for Patients in Slovakia with Advanced Diabetic Foot Ulcer

Objectives: To explore whether Heberprot-P (an epidermal growth factor) is a cost-effective option for the treatment of advanced diabetic foot ulcer as an add-on therapy to good wound care (GWC) in Slovakia from the perspective of health care payers. Methods: A Markov model was constructed to compare the costs and effects of Heberprot-P plus GWC to those of GWC alone from the perspective of health care payers. The 52-week clinical trial period was extended to five- and 10-year time horizons. Transition probabilities were calculated based on a previous clinical trial of Heberprot, utility values were derived from the scientific literature, and cost vectors were collected from the General Health Insurance Fund database in Slovakia. A one-way deterministic sensitivity analysis was employed to explore the influence of uncertainty for each input parameter on the incremental cost-effectiveness ratio (ICER). Results: Based on the ICER threshold of €30,030 per quality-adjusted life year (QALY) recommended by the Slovak Ministry of Health, Heberprot-P therapy plus GWC is not a cost-effective alternative to GWC alone over a 10-year time horizon. The ICER increases if a longer time horizon is applied, as the incremental costs are similar, but the aggregated utility gain from avoided amputation is lower. Based on the sensitivity analysis, the utility multiplier for the health state “no ulcer after small amputation” had the most impact on the ICER; however, the model was robust to changes in all input parameters. Conclusions: Heberprot-P, as an add-on therapy to GWC in the treatment of advanced diabetic foot ulcer, is not a cost-effective alternative to GWC alone. However, if the unit cost of Heberprot-P were to be reduced to <€273, its ICER would be <€30,030

Transanal Minimally Invasive Surgery: A Promising Alternative for Certain Advanced Rectal Cancer Patients

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An integrated circuit for chip-based analysis of enzyme kinetics and metabolite quantification

We have created a novel chip-based diagnostic tools based upon quantification of metabolites using enzymes specific for their chemical conversion. Using this device we show for the first time that a solid-state circuit can be used to measure enzyme kinetics and calculate the Michaelis-Menten constant. Substrate concentration dependency of enzyme reaction rates is central to this aim. Ion-sensitive field effect transistors (ISFET) are excellent transducers for biosensing applications that are reliant upon enzyme assays, especially since they can be fabricated using mainstream microelectronics technology to ensure low unit cost, mass-manufacture, scaling to make many sensors and straightforward miniaturisation for use in point-of-care devices. Here, we describe an integrated ISFET array comprising 216 sensors. The device was fabricated with a complementary metal oxide semiconductor (CMOS) process. Unlike traditional CMOS ISFET sensors that use the Si3N4 passivation of the foundry for ion detection, the device reported here was processed with a layer of Ta2O5 that increased the detection sensitivity to 45 mV/pH unit at the sensor readout. The drift was reduced to 0.8 mV/hour with a linear pH response between pH 2 – 12. A high-speed instrumentation system capable of acquiring nearly 500 fps was developed to stream out the data. The device was then used to measure glucose concentration through the activity of hexokinase in the range of 0.05 mM – 231 mM, encompassing glucose’s physiological range in blood. Localised and temporal enzyme kinetics of hexokinase was studied in detail. These results present a roadmap towards a viable personal metabolome machine

Tracking the contribution of inductive bias to individualised internal models

Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times. We demonstrate that the approach is capable of characterizing the discrepancy between the internal model maintained by individuals and the ideal observer model and to track the evolution of the contribution of the ideal observer model to the internal model throughout training. In particular, in an implicit visuomotor sequence learning task the identified discrepancy revealed an inductive bias that was consistent across individuals but varied in strength and persistence

Introduction to special section: Building complex and realistic geological models from sparse data

International audienceEarth scientists have always created spatial models of the subsurface. Before the dawn of computer-based modeling, earth models were simply drawn by hand on a piece of paper as cross section or plan views, sometimes utilizing the techniques of descriptive geometry. These hand-draw models are quick and easy to create; this is why we are still doing them on white boards, note books and sometimes even on napkins. They communicate ideas very well, but they are subjective and rarely constrained by data in a measurable way. As the number of observations grew with the advancement of data collections technologies, the possibility to use mathematical algorithms to do the modeling became a reality. These processes, first applied in 2D then in 3D, removed some of the subjectivity from the modeling. These processes work very well when the data density is high enough, meaning that models built with different mathematical methods are both realistic and similar one to another