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Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings show that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding. Reconstructing imagined speech from neural activity holds great promises for people with severe speech production deficits. Here, the authors demonstrate using human intracranial recordings that both low- and higher-frequency power and local cross-frequency contribute to imagined speech decoding.

Since the 2013–2014 Ebola virus disease outbreak, Guinea has faced recurrent epidemics of viral hemorrhagic fevers. Although the country has learned from these epidemics by improving its disease surveillance and investigation capacities, local authorities and stakeholders, including community actors, are not sufficiently involved in the disease-emergence response. As a result, measures are not fully understood and have failed to engage local stakeholders. However, recent research has shown community-based response measures to be effective. For this study, we used a qualitative participatory research approach to (i) describe and analyze the health signals that alert local stakeholders to a problem, (ii) describe the outbreak response measures implemented in Guinée Forestière from local to national levels, and (iii) identify obstacles and levers for implementing responses adapted to the local sociocultural context. Local stakeholders receive a variety of health, environmental, and sociopolitical signals. When dealing with health signals, their next step should be to follow a flowchart developed using a top-down approach and disseminated by national stakeholders. However, our interviews revealed that local stakeholders found this official flowchart difficult to understand. To address this issue, we used a bottom-up approach to co-construct with local stakeholders a response flowchart based on their perceptions and experiences. The resulting diagram opens the door to the development of a community-based response. We then identified six main obstacle categories from the interviews, including insufficient logistical and financial resources, lack of legitimacy of community workers, and inadequate coordination. Based on these obstacles, we suggest ways to develop a response to emerging zoonotic diseases that would enable local stakeholders to better understand their roles and responsibilities and improve their commitment to the outbreak response. Ultimately, this study should help to build an integrated, community-based early warning and response system in Guinée Forestière.

A bstract In this work, we study the lepton number violating tau decays via intermediate on-shell Majorana neutrinos N j into two scalar mesons and a lepton τ ±  →  M 1 ± N j  →  M 1 ± M 2 ± ℓ ∓ . We calculate the Branching ratios Br ( τ ± ) and the CP asymmetry (Γ( τ + ) − Γ( τ − )) / (Γ( τ + ) + Γ( τ − )) for such decays, in a scenario that contains at least two heavy Majorana neutrinos. The results show that the CP asymmetry is small, but becomes comparable with the branching ratio Br( τ ± ) when their mass difference is similar with their decay width Δ M N ∼ Γ N . We also present regions of the heavy-light neutrino mixing elements, in which the CP asymmetry could be explored in future tau factories.

Objective: This paper examines the Merdeka Curriculum as a response to Indonesian education policy challenges in the digital era and globalization. The policy aims to enhance the flexibility and quality of education through a more adaptive approach to student needs and technological advancements.   Theoretical Framework: The study is grounded in public policy adaptation theory and 21st-century education theory, which emphasizes critical thinking, creativity, collaboration, and digital literacy. This framework helps understand how the Merdeka Curriculum is implemented within the Indonesian education context.   Method: A qualitative approach is used in this study, employing a case study method in several schools in Indonesia. Data were collected through in-depth interviews, classroom observations, and policy document analysis. Participants included teachers, students, and education policymakers.   Results and Conclusions: The findings indicate that the Merdeka Curriculum provides greater flexibility for teachers in designing learning programs tailored to students' needs. However, implementation in the field faces various challenges, such as lack of technological infrastructure and teacher training. In conclusion, while the Merdeka Curriculum has the potential to improve educational quality, stronger support in technology and training is necessary for its success.   Research Implications: This study has significant implications for the development of education policy in Indonesia, especially in the digital era. The findings encourage policymakers to consider greater investments in educational technology and teacher training to ensure the success of the Merdeka Curriculum.   Originality/Value: This paper offers an original contribution by linking the Merdeka Curriculum policy to the challenges and opportunities of the digital era, providing practical insights into the implementation of education policy in Indonesia. It also adds to the literature on education policy adaptation in developing countries.

Kinetic models are essential to quantitatively understand and predict the behaviour of metabolic networks. Detailed and thermodynamically feasible kinetic models of metabolism are inherently difficult to formulate and fit. They have a large number of heterogeneous parameters, are non-linear and have complex interactions. Many powerful fitting strategies are ruled out by the intractability of the likelihood function. Here, we have developed a computational framework capable of fitting feasible and accurate kinetic models using Approximate Bayesian Computation. This framework readily supports advanced modelling features such as model selection and model-based experimental design. We illustrate this approach on the tightly-regulated mammalian methionine cycle. Sampling from the posterior distribution, the proposed framework generated thermodynamically feasible parameter samples that converged on the true values and displayed remarkable prediction accuracy in several validation tests. Furthermore, a posteriori analysis of the parameter distributions enabled appraisal of the systems properties of the network (e.g., control structure) and key metabolic regulations. Finally, the framework was used to predict missing allosteric interactions.

Predicting the students’ performance has become a challenging task due to the increasing amount of data in educational systems. In keeping with this, identifying the factors affecting the students’ performance in higher education, especially by using predictive data mining techniques, is still in short supply. This field of research is usually identified as educational data mining. Hence, the main aim of this study is to identify the most commonly studied factors that affect the students’ performance, as well as, the most common data mining techniques applied to identify these factors. In this study, 36 research articles out of a total of 420 from 2009 to 2018 were critically reviewed and analyzed by applying a systematic literature review approach. The results showed that the most common factors are grouped under four main categories, namely students’ previous grades and class performance, students’ e-Learning activity, students’ demographics, and students’ social information. Additionally, the results also indicated that the most common data mining techniques used to predict and classify students’ factors are decision trees, Naïve Bayes classifiers, and artificial neural networks.

Background Uniform random sampling of mass-balanced flux solutions offers an unbiased appraisal of the capabilities of metabolic networks. Unfortunately, it is impossible to avoid thermodynamically infeasible loops in flux samples when using convex samplers on large metabolic models. Current strategies for randomly sampling the non-convex loopless flux space display limited efficiency and lack theoretical guarantees. Results Here, we present LooplessFluxSampler, an efficient algorithm for exploring the loopless mass-balanced flux solution space of metabolic models, based on an Adaptive Directions Sampling on a Box (ADSB) algorithm. ADSB is rooted in the general Adaptive Direction Sampling (ADS) framework, specifically the Parallel ADS, for which theoretical convergence and irreducibility results are available for sampling from arbitrary distributions. By sampling directions that adapt to the target distribution, ADSB traverses more efficiently the sample space achieving faster mixing than other methods. Importantly, the presented algorithm is guaranteed to target the uniform distribution over convex regions, and it provably converges on the latter distribution over more general (non-convex) regions provided the sample can have full support. Conclusions LooplessFluxSampler enables scalable statistical inference of the loopless mass-balanced solution space of large metabolic models. Grounded in a theoretically sound framework, this toolbox provides not only efficient but also reliable results for exploring the properties of the almost surely non-convex loopless flux space. Finally, LooplessFluxSampler includes a Markov Chain diagnostics suite for assessing the quality of the final sample and the performance of the algorithm.

Although Weber’s law is the most firmly established regularity in sensation, no principled way has been identified to choose between its many proposed explanations. We investigated Weber’s law by training rats to discriminate the relative intensity of sounds at the two ears at various absolute levels. These experiments revealed the existence of a psychophysical regularity, which we term time–intensity equivalence in discrimination (TIED), describing how reaction times change as a function of absolute level. The TIED enables the mathematical specification of the computational basis of Weber’s law, placing strict requirements on how stimulus intensity is encoded in the stochastic activity of sensory neurons and revealing that discriminative choices must be based on bounded exact accumulation of evidence. We further demonstrate that this mechanism is not only necessary for the TIED to hold but is also sufficient to provide a virtually complete quantitative description of the behavior of the rats.