Interactive apps promote learning of basic mathematics in children with special educational needs and disabilities

Interactive apps delivered on touch-screen tablets can be effective at supporting the acquisition of basic skills in mainstream primary school children. This technology may also be beneficial for children with Special Educational Needs and Disabilities (SEND) as it can promote high levels of engagement with the learning task and an inclusive learning environment. However, few studies have measured extent of learning for SEND pupils when using interactive apps, so it has yet to be determined if this technology is effective at raising attainment for these pupils. We report the first observational study of a group of 33 pupils with SEND from two primary schools in Malawi that are implementing a new digital technology intervention which uses touch-screen tablets to deliver interactive apps designed to teach basic mathematical skills. The apps contain topics that align to the national curriculum. To assess learning gains, rate of progress (minutes per topic) for each pupil was determined by calculating the average time taken to complete a topic. Progress rate was then correlated with teacher ratings of extent of disability and independent ratings of pupil engagement with the apps. Results showed SEND pupils could interact with the apps and all pupils passed at least one topic. Average progress rate for SEND pupils was twice as long as mainstream peers. Stepwise regression revealed extent of disability significantly predicted progress rate. Further exploratory correlations revealed pupils with moderate to severe difficulties with hearing and/or language made slower progress through the apps than those with greater functionality in these two domains because the use of verbal instructions within the apps limited their capacity to learn. This original quantitative analysis demonstrates that interactive apps can raise learning standards in pupils with SEND butmay have limited utility for pupils with severe difficulties. Softwaremodifications are needed to address specific areas of difficulty preventing pupils from progressing

Slow dynamics in structured neural network models

Humans and some other animals are able to perform tasks that require coordination of movements across multiple temporal scales, ranging from hundreds of milliseconds to several seconds. The fast timescale at which neurons naturally operate, on the order of tens of milliseconds, is well-suited to support motor control of rapid movements. In contrast, to coordinate movements on the order of seconds, a neural network should produce reliable dynamics on a similarly âslowâ timescale. Neurons and synapses exhibit biophysical mechanisms whose timescales range from tens of milliseconds to hours, which suggests a possible role of these mechanisms in producing slow reliable dynamics. However, how such mechanisms influence network dynamics is not yet understood. An alternative approach to achieve slow dynamics in a neural network consists in modifying its connectivity structure. Still, the limitations of this approach and in particular to what degree the weights require fine-tuning, remain unclear. Understanding how both the single neuron mechanisms and the connectivity structure might influence the network dynamics to produce slow timescales is the main goal of this thesis. We first consider the possibility of obtaining slow dynamics in binary networks by tuning their connectivity. It is known that binary networks can produce sequential dynamics. However, if the sequences consist of random patterns, the typical length of the longest sequence that can be produced grows linearly with the number of units. Here, we show that we can overcome this limitation by carefully designing the sequence structure. More precisely, we obtain a constructive proof that allows to obtain sequences whose length scales exponentially with the number of units. To achieve this however, one needs to exponentially fine-tune the connectivity matrix. Next, we focus on the interaction between single neuron mechanisms and recurrent dynamics. Particular attention is dedicated to adaptation, which is known to have a broad range of timescales and is therefore particularly interesting for the subject of this thesis. We study the dynamics of a random network with adaptation using mean-field techniques, and we show that the network can enter a state of resonant chaos. Interestingly, the resonance frequency of this state is independent of the connectivity strength and depends only on the properties of the single neuron model. The approach used to study networks with adaptation can also be applied when considering linear rate units with an arbitrary number of auxiliary variables. Based on a qualitative analysis of the mean-field theory for a random network whose neurons are described by a D -dimensional rate model, we conclude that the statistics of the chaotic dynamics are strongly influenced by the single neuron model under investigation. Using a reservoir computing approach, we show preliminary evidence that slow adaptation can be beneficial when performing tasks that require slow timescales. The positive impact of adaptation on the network performance is particularly strong in the presence of noise. Finally, we propose a network architecture in which the slowing-down effect due to adaptation is combined with a hierarchical structure, with the purpose of efficiently generate sequences that require multiple, hierarchically organized timescales

Diversity in a honey bee pathogen: first report of a third master variant of the Deformed Wing Virus quasispecies

Treatment of emerging RNA viruses is hampered by the high mutation and replication rates that enable these viruses to operate as a quasispecies. Declining honey bee populations have been attributed to the ectoparasitic mite Varroa destructor and its affiliation with Deformed Wing Virus (DWV). In the current study we use next-generation sequencing to investigate the DWV quasispecies in an apiary known to suffer from overwintering colony losses. We show that the DWV species complex is made up of three master variants. Our results indicate that a new DWV Type C variant is distinct from the previously described types A and B, but together they form a distinct clade compared with other members of the Iflaviridae. The molecular clock estimation predicts that Type C diverged from the other variants ~319 years ago. The discovery of a new master variant of DWV has important implications for the positive identification of the true pathogen within global honey bee populations

Risk factors for sporadic norovirus infection: a systematic review and meta-analysis

Norovirus is responsible for 20% of acute gastroenteritis worldwide. The fecal-oral route of transmission is known, but we proposed a first attempt to identify the relative importance of different sources and vehicles for sporadic cases using meta-analysis models. Case-control and cohort/cross-sectional studies were systematically reviewed and analyzed to assess the main risk factors associated with sporadic norovirus infections. Suitable scientific articles were identified through systematic literature search and subjected to a methodological quality assessment. Mixed-effects meta-analyses models were adjusted by population type to appropriate risk factor categories. The quality assessment stage led to include 14 primary studies conducted between 1993 and 2014. From these, eight studies investigated exposures in children/infants, and eight concerned the mixed population. The meta-analysis confirmed the oro-fecal route for norovirus infections, with the person-to-person transmission (pooled OR=3.002; 95% CI: [2.502 -3.060] in mixed population), and the lack of personal hygiene (pooled OR=2.329; 95% CI: [1.048 -5.169]). The meta-analysis also enlightened the role of indirect transmission through the environment with pathways like untreated drinking water (mixed population), with a pooled OR=2.680 (95% CI: [1.081-6.643]) and farm environment (children population). Indirect transmission also involved the food pathway, which was finally found significant with consumption of seafood (mixed population) (pooled OR=2.270; 95% CI: [1.299-3.968]) and composite food (eating outside/uncooked mixed and young population) (pooled OR=4.541; 95% CI: [3.461-5.958]). These results are coherent with the findings from studies on outbreaks. However, a too broad definition of exposure factors limited the interpretation of results, as occurred with the seafood pathways that combined fish and shellfish. Other factors such as consumption of Food-handled products or the type of drinking water deserveE to be better investigated. Furthermore, better harmonization in case definition and appropriate case-control or cross-sectional studies would allow better addressing sporadic cases risk factors, especially for susceptible populations, such as children, elderly or immunosuppressed persons.U. Gonzales-Barron and V. Cadavez are grateful to the Foundation for Food Science and Technology (FCT, Portugal) and FEDER under Programme PT2020 for financial support to CIMO (UID/AGR/00690/ 2019). U. Gonzales-Barron thanks the national funding by FCT, P.I., through the Institutional Scientific Employment Program contract.info:eu-repo/semantics/publishedVersio

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