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This study analyzes the effects of COVID-19 confinement on the autonomous learning performance of students in higher education. Using a field experiment with 458 students from three different subjects at Universidad Autónoma de Madrid (Spain), we study the differences in assessments by dividing students into two groups. The first group (control) corresponds to academic years 2017/2018 and 2018/2019. The second group (experimental) corresponds to students from 2019/2020, which is the group of students that had their face-to-face activities interrupted because of the confinement. The results show that there is a significant positive effect of the COVID-19 confinement on students' performance. This effect is also significant in activities that did not change their format when performed after the confinement. We find that this effect is significant both in subjects that increased the number of assessment activities and subjects that did not change the student workload. Additionally, an analysis of students' learning strategies before confinement shows that students did not study on a continuous basis. Based on these results, we conclude that COVID-19 confinement changed students' learning strategies to a more continuous habit, improving their efficiency. For these reasons, better scores in students' assessment are expected due to COVID-19 confinement that can be explained by an improvement in their learning performance.

Students’ motivation is one of the most relevant factors when improving the quality of the learning process. In this context, gamification is a powerful tool for increasing motivation at all levels of teaching. Since gamification methodologies can be applied in many different ways, personalizing gamified activities as a function of gamification user types is a promising strategy. Knowing the user types is also an advantage to understand the nature of learners in the class. In this article, we present the findings from several pilot exercises, where we identified gamification user types among students from Spain and Finland, analyzing their prevalence as a function of age, gender, country, and field of study. We also designed a gamification experience where activities were designed to fit the preferences of different user types. From these pilot experiences, we found that gamification user types are only relevant when other variables, such as the difficulty of the tasks or the presence of students who do not work well in groups, are not present. Based on our findings, we conclude that distance learning and subjects where previous knowledge is not present are good choices when gamifying a subject.

A study of the electrostatic force between an Electrostatic Force Microscope tip and a dielectric thin film with finite conductivity is presented. By using the Thomas-Fermi approximation and the method of image charges, we calculate the electrostatic potential and force as a function of the thin film screening length, which is a magnitude related to the amount of free charge in the thin film and is defined as the maximum length that the electric field is able to penetrate in the sample. We show the microscope’s signal on dielectric films can change significantly in the presence of a finite conductivity even in the limit of large screening lengths. This is particularly relevant in determining the effective dielectric constant of thin films from Electrostatic Force Microscopy measurements. According to our model, for example, a small conductivity can induce an error of more than two orders of magnitude in the determination of the dielectric constant of a material. Finally, we suggest a method to discriminate between permittivity and conductivity effects by analyzing the dependence of the signal with the tip-sample distance.

A method that allows us to give a different treatment to any neuron inside feedforward neural networks is presented. The algorithm has been implemented with two very different learning methods: a standard Back-propagation (BP) procedure and an evolutionary algorithm. First, we have demonstrated that the EA training method converges faster and gives more accurate results than BP. Then we have made a full analysis of the effects of turning off different combinations of neurons after the training phase. We demonstrate that EA is much more robust than BP for all the cases under study. Even in the case when two hidden neurons are lost, EA training is still able to give good average results. This difference implies that we must be very careful when pruning or redundancy effects are being studied since the network performance when losing neurons strongly depends on the training method. Moreover, the influence of the individual inputs will also depend on the training algorithm. Since EA keeps a good classification performance when units are lost, this method could be a good way to simulate biological learning systems since they must be robust against deficient neuron performance. Although biological systems are much more complex than the simulations shown in this article, we propose that a smart training strategy such as the one shown here could be considered as a first protection against the losing of a certain number of neurons.

This study explores the effects of COVID-19 confinement in the students performance in higher education. Using a field experiment of 458 students from three different subjects in Universidad Autonoma de Madrid (Spain), we study the differences in assessments by dividing students into two groups. The first group (control) corresponds to academic years 2017/2018 and 2018/2019. The second group (experimental) corresponds to students from 2019/2020, which is the group of students that interrupted their face-to-face activities because of the confinement. The results show that there is a significant positive effect of the COVID-19 confinement on students performance. This effect is also significative in activities that did not change their format when performed after the confinement. We find that this effect is significative both in subjects that increased the number of assessment activities and subjects that did not change the workload of students. Additionally, an analysis of students learning strategies before confinement shows that students did not study in a continuous basis. Based on these results, we conclude that COVID-19 confinement changed students learning strategies to a more continuous habit, improving their efficiency. For these reasons, better scores in students assessment are expected due to COVID-19 confinement that can be explained by an improvement in their learning performance.

In this work, we show a methodology aimed to improve the quality of the assessment process for subjects related to basic programming. The method takes into account the relevance of the items and the students answers to follow different paths to improve the accuracy of the assessment process. We have developed numerical simulations and experiments with real students that demonstrate the advantages of this model when compared with traditional evaluation tools. This method improves the objectiveness and takes into account the relevance of the subject contents. We also demonstrate that the architecture of the algorithm is fully compatible with traditional multiple choice test formalisms. Our results can be directly used in computer-assisted tests for different subjects and disciplines, as well as used by the students as a self-evaluation tool with the objective of correcting their deficiencies in the learning process.

Microorganisms in the human intestine (i.e. the gut microbiome) have an increasingly recognized impact on human health, including brain functioning. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with abnormalities in dopamine neurotransmission and deficits in reward processing and its underlying neuro-circuitry including the ventral striatum. The microbiome might contribute to ADHD etiology via the gut-brain axis. In this pilot study, we investigated potential differences in the microbiome between ADHD cases and undiagnosed controls, as well as its relation to neural reward processing. We used 16S rRNA marker gene sequencing (16S) to identify bacterial taxa and their predicted gene functions in 19 ADHD and 77 control participants. Using functional magnetic resonance imaging (fMRI), we interrogated the effect of observed microbiome differences in neural reward responses in a subset of 28 participants, independent of diagnosis. For the first time, we describe gut microbial makeup of adolescents and adults diagnosed with ADHD. We found that the relative abundance of several bacterial taxa differed between cases and controls, albeit marginally significant. A nominal increase in the Bifidobacterium genus was observed in ADHD cases. In a hypothesis-driven approach, we found that the observed increase was linked to significantly enhanced 16S-based predicted bacterial gene functionality encoding cyclohexadienyl dehydratase in cases relative to controls. This enzyme is involved in the synthesis of phenylalanine, a precursor of dopamine. Increased relative abundance of this functionality was significantly associated with decreased ventral striatal fMRI responses during reward anticipation, independent of ADHD diagnosis and age. Our results show increases in gut microbiome predicted function of dopamine precursor synthesis between ADHD cases and controls. This increase in microbiome function relates to decreased neural responses to reward anticipation. Decreased neural reward anticipation constitutes one of the hallmarks of ADHD.

HIV infection induces phenotypic and functional changes to CD8+ T cells defined by the coordinated upregulation of a series of negative checkpoint receptors that eventually result in T cell exhaustion and failure to control viral replication. We report that effector CD8+ T cells during HIV infection in blood and SIV infection in lymphoid tissue exhibit higher levels of the negative checkpoint receptor TIGIT. Increased frequencies of TIGIT+ and TIGIT+ PD-1+ CD8+ T cells correlated with parameters of HIV and SIV disease progression. TIGIT remained elevated despite viral suppression in those with either pharmacological antiretroviral control or immunologically in elite controllers. HIV and SIV-specific CD8+ T cells were dysfunctional and expressed high levels of TIGIT and PD-1. Ex-vivo single or combinational antibody blockade of TIGIT and/or PD-L1 restored viral-specific CD8+ T cell effector responses. The frequency of TIGIT+ CD4+ T cells correlated with the CD4+ T cell total HIV DNA. These findings identify TIGIT as a novel marker of dysfunctional HIV-specific T cells and suggest TIGIT along with other checkpoint receptors may be novel curative HIV targets to reverse T cell exhaustion.

As the increasing prevalence of type 2 diabetes mellitus has put pressure on health systems to appropriately manage these patients, there have been a growing number of mobile apps designed to improve the self-management of diabetes. One such app, BlueStar, has been shown to significantly reduce hemoglobin A (HbA ) levels in small studies and is the first app in the United States to receive Food and Drug Administration approval as a mobile prescription therapy. However, the impact of the app across real-world population among different clinical sites and health systems remains unclear. The primary objective of this study was to conduct a pragmatic randomized controlled trial of the BlueStar mobile app to determine if app usage leads to improved HbA levels among diverse participants in real-life clinical contexts. We hypothesized that this mobile app would improve self-management and HbA levels compared with controls. The study consisted of a multicenter pragmatic randomized controlled trial. Overall, 110 participants randomized to the immediate treatment group (ITG) received the intervention for 6 months, and 113 participants randomized to the wait-list control (WLC) group received usual care for the first 3 months and then received the intervention for 3 months. The primary outcome was glucose control measured by HbA levels at 3 months. Secondary outcomes assessed intervention impact on patient self-management, experience of care, and self-reported health utilization using validated scales, including the Problem Areas in Diabetes, the Summary of Diabetes Self-Care Activities, and the EuroQol-5D. Intervention usage data were collected directly from the app. The results of an analysis of covariance controlling for baseline HbA levels did not show evidence of intervention impact on HbA levels at 3 months (mean difference [ITG-WLC] -0.42, 95% CI -1.05 to 0.21; P=.19). Similarly, there was no intervention effect on secondary outcomes measuring diabetes self-efficacy, quality of life, and health care utilization behaviors. An exploratory analysis of 57 ITG participants investigating the impact of app usage on HbA levels showed that each additional day of app use corresponded with a 0.016-point decrease in participants' 3-month HbA levels (95% CI -0.03 to -0.003). App usage varied significantly by site, as participants from 1 site logged in to the app a median of 36 days over 14 weeks (interquartile range [IQR] 10.5-124); those at another site used the app significantly less (median 9; IQR 6-51). The results showed no difference between intervention and control arms for the primary clinical outcome of glycemic control measured by HbA levels. Although there was low usage of the app among participants, results indicate contextual factors, particularly site, had a significant impact on overall usage. Future research into the patient and site-specific factors that increase app utilization are needed. Clinicaltrials.gov NCT02813343; https://clinicaltrials.gov/ct2/show/NCT02813343 (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT02813343).

This systematic review and meta-analysis examined the dose-response relationship between exercise and cognitive function in older adults with and without cognitive impairments. We included single-modality randomized controlled aerobic, anaerobic, multicomponent or psychomotor exercise trials that quantified training frequency, session and program duration and specified intensity quantitatively or qualitatively. We defined total exercise duration in minutes as the product of program duration, session duration, and frequency. For each study, we grouped test-specific Hedges' d (n = 163) and Cohen's d (n = 23) effect sizes in the domains Global cognition, Executive function and Memory. We used multilevel mixed-effects models to investigate dose-related predictors of exercise effects. In healthy older adults (n = 23 studies), there was a small positive effect of exercise on executive function (d = 0.27) and memory (d = 0.24), but dose-parameters did not predict the magnitude of effect sizes. In older adults with cognitive impairments (n = 13 studies), exercise had a moderate positive effect on global cognition (d = 0.37). For older adults with cognitive impairments, we found evidence for exercise programs with a short session duration and high frequency to predict higher effect sizes (d = 0.43-0.50). In healthy older adults, dose-parameters did not predict the magnitude of exercise effects on cognition. For older adults with cognitive impairments, exercise programs with shorter session duration and higher frequency may generate the best cognitive results. Studies are needed in which different exercise doses are directly compared among randomized subjects or conditions.