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Funding Acknowledgements Type of funding sources: None. Introduction Electrocardiogram (ECG) is the most performed cardiovascular tool in medicine(1). First described by Einthoven at the beginning of the 20th century, it is now a useful, cheap and safe exam, accessible to millions of people on devices as light as smartwatches. Several studies have demonstrated the variability in the ECG interpretation. A recent systematic review described a low median accuracy of 54%, across different training levels (from students to experimented Cardiologists), and suggested that continuous training with courses, software and databases significantly improves that accuracy(2). With the easy access to this exam, along with the high prevalence of arrhythmias, Primary Care Physicians play an important role in this ECG era. Nevertheless, some articles reported that they too make frequent errors in interpretation and feel "uncomfortable" dealing with the 120 diagnostic ECG patterns described(3). Purpose To develop a website designed to aid Primary Care Physicians in diagnosing and managing patients with most ECG diagnostic patterns. Methods Literature regarding ECG pattern diagnosis and management was reviewed. The research combined European and North American guidelines, medical textbooks and websites, reviews and original articles available on Pubmed. The website information was assembled and written by Primary Care Physicians, then reviewed by Cardiologists that work in the Arrhythmology area. Results It was created an open, free access, nonprofitable, continuously updated website, written in Portuguese, that has 4 main components: 1. A Homepage with a search toolbar; 2. An "Index Page", with all the ECG diagnostic statements organized as a book index (FIGURE1); 3. A "Toolkit" link, with tools that are useful in ECG management such as CHA2DDS2-VASc and HASBLED scores, doses and contraindications of drugs, etc (FIGURE2).; 4. When searching for an ECG pattern, the user can access a summarized text with its definition, causes, exams that might be required (e.g. echocardiogram), and management (FIGURE1). The management section organizes procedures from the most to the less urgent. A version of the website is available and fully functional for testing purposes. The website will be continuously updated with images of all ECG diagnostic patterns from a local hospital database and specific information for pediatrics, pregnant women, and athletes. Conclusion Nowadays, Primary Care Physicians face a fascinating era of increasing amounts of information, fast discoveries and possibilities that demand permanent attention and update. The ECG is a complex but valuable and accessible tool with more than 100 different diagnostic statements. With this website, the aim is to empower Primary Care Physicians so they can confidently interpret ECG, and also manage patients according to the best medical recommendations available, with an updated tool fully designed for them.

The synaptic balance between excitation and inhibition (E/I balance) is a fundamental principle of cortical circuits, and disruptions in E/I balance are commonly linked to cognitive deficits such as impaired decision-making. Explanatory gaps remain in a mechanistic understanding of how E/I balance contributes to cognitive computations, and how E/I disruptions at the synaptic level can propagate to induce behavioral deficits. Here, we studied how E/I perturbations may impair perceptual decision-making in a biophysically-based association cortical circuit model. We found that both elevating and lowering E/I ratio, via NMDA receptor (NMDAR) hypofunction at inhibitory interneurons and excitatory pyramidal neurons, respectively, can similarly impair psychometric performance, following an inverted-U dependence. Nonetheless, these E/I perturbations differentially alter the process of evidence accumulation across time. Under elevated E/I ratio, decision-making is impulsive, overweighting early evidence and underweighting late evidence. Under lowered E/I ratio, decision-making is indecisive, with both evidence integration and winner-take-all competition weakened. The distinct time courses of evidence accumulation at the circuit level can be measured at the behavioral level, using multiple psychophysical task paradigms which provide dissociable predictions. These results are well captured by a generalized drift-diffusion model (DDM) with self-coupling, implementing leaky or unstable integration, which thereby links biophysical circuit modeling to algorithmic process modeling and facilitates model fitting to behavioral choice data. In general, our findings characterize critical roles of cortical E/I balance in cognitive function, bridging from biophysical to behavioral levels of analysis.SIGNIFICANCE STATEMENTCognitive deficits in multiple neuropsychiatric disorders, including schizophrenia, have been associated with alterations in the balance of synaptic excitation and inhibition (E/I) in cerebral cortical circuits. However, the circuit mechanisms by which E/I imbalance leads to cognitive deficits in decision-making have remained unclear. We used a computational model of decision-making in cortical circuits to study the neural and behavioral effects of E/I imbalance. We found that elevating and lowering E/I ratio produce distinct modes of dysfunction in decision-making processes, which can be dissociated in behavior through psychophysical task paradigms. The biophysical circuit model can be mapped onto a psychological model of decision-making which can facilitate experimental tests of model predictions.

Astrocytes are active cells that respond to neurotransmitters with elevations in their intracellular calcium concentration (calcium signals). In a tripartite synapse involving two neurons coupled by a glutamatergic synapse and one astrocyte, glutamate released by the presynaptic neuron can generate calcium signals in the astrocyte, which in turn trigger the release of neuroactive molecules (gliotransmitters) by the astrocyte that bind to receptors in the pre- and postsynaptic neuron membranes and modulate synaptic transmission. Astrocytic calcium signals can also be evoked by dopamine released in distant sites. Little is known about how dopamine modulates glutamatergic-evoked astrocyte activity. To investigate this question, we constructed compartmental astrocyte models with three different morphologies: linear (soma plus a single branch); branched (soma plus two branches); and bifurcated (soma plus a single branch that bifurcates into two branchlets). Compartments were modeled by conductance-based equations for membrane voltage and transport of ions, glutamate and dopamine between extra- and intracellular spaces. Glutamatergic and dopaminergic stimuli were modeled as Poisson processes with variable frequencies, and astrocyte responses were measured by number and location of evoked calcium signals. For cells with linear morphology, whole-cell dopaminergic stimulation reduced the glutamatergic stimulation frequency of distal compartments needed to generate calcium signals. For both the branched and bifurcated morphologies, whole-cell dopaminergic stimulation together with glutamatergic stimulation of one of the processes reduced the glutamatergic stimulation frequency necessary to trigger a calcium signal in the other process. The same glutamatergic stimulation protocols without dopamine stimulation required higher glutamatergic input frequencies to evoke calcium signals. Our results suggest that dopamine facilitates the occurrence of glutamatergic-evoked calcium signals, and that dopamine-glutamate interaction can control the distribution of calcium signals along the astrocyte extension.Author summaryAstrocytes are brain cells that are not electrically excitable as neurons but display chemical excitability in the form of transient rises in the intracellular calcium concentration (calcium signals) evoked by neurotransmitters. A tripartite synapse consists of pre- and postsynaptic terminals ensheathed by astrocyte processes. Neurotransmitters released by the presynaptic neuron can generate calcium signals in the astrocyte, which in turn trigger the release of neuroactive molecules (gliotransmitters) by the astrocyte that bind to receptors in the pre- and postsynaptic membranes and modulate synaptic transmission. Two neurotransmitters that can evoke astrocytic calcium signals are glutamate, the major neurotransmitter of excitatory synapses, and dopamine, an important modulatory neurotransmitter that can diffuse to wider regions than the synaptic release site. Little is known about how dopamine modulates glutamatergic-evoked astrocyte activity, and here we investigate this question using computational modeling. We constructed compartmental astrocyte models with three different morphologies: linear, with a single branch emanating from soma; branched, with two branches emanating from soma; and bifurcated, with a branch emanating from soma that bifurcates into two branchlets. Compartments were modeled by conductance-based equations for membrane voltage and transport of ions (sodium, potassium and calcium), glutamate and dopamine between extra- and intracellular spaces. Glutamatergic and dopaminergic stimuli were modeled as Poisson processes with variable frequencies. Astrocyte models with the three morphologies were submitted to similar stimulation protocols to compare their responses, which were measured in terms of the frequency and location of evoked calcium signals. For cells with linear morphology, dopaminergic stimulation of the entire cell (to simulate the diffuse action of dopamine) reduced the glutamatergic stimulation frequency of distal compartments (which simulates glutamatergic input from presynaptic neuron) needed to generate calcium signals. For both the branched and bifurcated morphologies, dopaminergic stimulation of the whole cell together with glutamatergic stimulation of the distal portions of one of the processes reduced the glutamate stimulation frequency necessary to trigger a calcium signal in the distal portions of the other process. Repetitions of the glutamatergic stimulation protocols without whole cell dopaminergic stimulation showed that higher glutamatergic input frequencies were needed to evoke calcium signals. Our results suggest that dopamine facilitates the occurrence of calcium signals evoked by glutamatergic inputs, and that interaction between dopamine and glutamate can control the distribution of calcium signals along the astrocyte extension.

Introduction: Treatment with ulipristal acetate effectively controls excessive bleeding due to uterine fibroids and reduces their size. Uterine fibroid size reduction is expected to improve the results of the myomectomy and the reproductive prospects of the patient. Methods: Retrospective and descriptive analysis of a series of 53 patients who achieved pregnancy after being treated for symptomatic uterine fibroids. The primary endpoints were pregnancy and birth outcomes in women with symptomatic uterine fibroids that conceived following at least one course of therapy with ulipristal acetate 5 mg/day. The secondary endpoints were time until pregnancy, reasons for ulipristal acetate treatment, number of treatment courses completed, hemorrhagic control achievement, hemoglobin levels, fibroid FIGO classification, largest fibroid diameter, and type of myomectomy (if any). Results: Fifty-five pregnancies were registered in 53 patients following ulipristal acetate therapy (43 live births, 9 miscarriages, and 3 ongoing pregnancies). Half of the patients became pregnant without interval surgery. Bleeding control was achieved in 96% of the cases. A significant increase (p Conclusion: So far, this is the largest case series reporting both pregnancy and birth outcomes following ulipristal acetate therapy for uterine fibroids. Our data support favorable outcomes after therapy for this population subset.

Polyphenols present in Suganda (Plectranthus amboinicus (Lour.) Spreng) extract can reduce the cationic iron to zero valent iron while simultaneously forming nanoparticles. The study involved the phenolic extraction at different solvent concentrations and temperatures. The synthesized Green--Nanoscale Zero Valent Iron (G--nZVI) and Green--Nanoscale Zero Valent Iron -- Copper Bimetal (G--nZVI--Cu) were used to reduce nitrate in water. Scanning Electron Microscopy -- Energy Dispersive X--ray (SEM--EDX) and X--ray Diffraction (XRD) are used to characterize the particles. It was found out that at 35°C and 90% ethanol, the highest phenolic content was obtained. The Analysis of Variance (ANOVA) results showed that the effect of temperature and solvent concentration is significant on the phenolic extraction with a probability value of < 0.0001. The SEM analysis showed that the particles have sizes ranging from 100-- 150 nm. The EDX analysis showed higher amounts of oxygen ranging from 50--60%, iron having 40--50% weight and copper with nearly 4--5% weight. The green synthesized particles are amorphous in structure, with no clear peak at 2θ = 45°. The synthesized particles were successfully used to reduce nitrate in water. Analysis showed that significant amount of nitrate was reduced after 60 minutes, having a removal efficiency of 84.36% and 85.81% for G--nZVI and G--nZVI--Cu, respectively. It was found out that ammonium and nitrogen gas were the main products of the reaction. The results show that G--nZVI and G--nZVI--Cu can be used for the remediation of nitrate in water due to its high removal efficiency.

Background Cardiovascular diseases are the leading cause of death worldwide and uncontrolled Arterial Hypertension (AH) is the most important involved risk factor. An observational study published in 2019 found differences between controlled and uncontrolled AH patients, namely less frequent chronotherapy and family history health records. We aimed to study medical designed population based interventions to reach an increase in controlled AH defining a set of growth dynamics (Δ) ≥ +5 in AH prevalence of control defined for a value of 140/90 mmHg. Methods 2019 results were presented in small medical teams weekly periodic scientific meetings and upon such awareness strategies were developed for improving AH control, according to the best practice evidence available. Data were collected, 6 months later, in a randomized representative sample of AH patients in each Primary Care Health Unit for a confidence interval 95%, error margin of 5%. Descriptive and inferential analysis, for a P < 0.001 and growth dynamics (Δ) were calculated. Results In a n = 148 sample there was a significant improvement in the rate of controlled hypertension (43.9% to 67.9%, P < 0.001, Δ = +54.0). Chronotherapy improved from 29.0% to 66.2% (P < 0.001), Δ = +128.3) and family medical records also improved (88.1% to 100%, Δ + 11.9). Conclusions Significant rise in the prevalence of AH control was achieved after informative and formative intervention. Chronotherapy was significantly improved, allowing a 67.9% AH control. Family medical records also significantly improved. Healthcare professional’s knowledge and reflection about their clinical activity seem to be important tools to achieve better results and hopefully health outcomes, in Arterial Hypertension.

Forecasting often involves multiple time-series that are hierarchically organized (e.g., sales by geography). In that case, there is a constraint that the bottom level forecasts add-up to the aggregated ones. Common approaches use traditional forecasting methods to predict all levels in the hierarchy and then reconcile the forecasts to satisfy that constraint. We propose a new algorithm that automatically forecasts multiple hierarchically organized time-series. We introduce a combination of additive Gaussian processes (GPs) with a hierarchical piece-wise linear function to estimate, respectively, the stationary and non-stationary components of the time-series. We define a flexible structure of additive GPs generated by each aggregated group in the hierarchy of the data. This formulation aims to capture the nested information in the hierarchy while avoiding overfitting. We extended the piece-wise linear function to be hierarchical by defining hyperparameters shared across related time-series. From our experiments, our algorithm can estimate hundreds of time-series at once. To work at this scale, the estimation of the posterior distributions of the parameters is performed using mean-field approximation. We validate the proposed method in two different real-world datasets showing its competitiveness when compared to the state-of-the-art approaches. In summary, our method simplifies the process of hierarchical forecasting as no reconciliation is required. It is easily adapted to non-Gaussian likelihoods and multiple or non-integer seasonalities. The fact that it is a Bayesian approach makes modeling uncertainty of the forecasts trivial.

This paper describes the main changes verified on the compulsory digital transition of teaching and learning in a mathematics curricular unit during the second semester of 2019–2020 academic year under COVID-19 pandemic. We highlighted the implementation process of online teaching-learning modes, including all procedures and practices that had been reshaped face to the new circumstances. Particular attention is paid to the main difficulties felt by the students and the teaching team in the adaptation phase to the new reality of distance learning. The analysis of outputs regarding the previous years and the comparison with the last assessment outputs lead us to conclude that the whole implementation has allowed mitigating an unfavourable scenario for educational success, in particular, the great social change in the student's lives. We also present the conclusions regarding the students inquiries, which were passed anonymously at the end of the semester, where both quantitative and qualitative approach to study the perceptions of students about the online teaching-learning modes. As we can see from the answers to this questionnaire, the students missed the face-to-face classes and the contact with teachers and colleagues. They also felt difficulties in the changes that were made in the assessment with a view to moving to the online regime, but they also felt that the course as a whole went well, as evidenced by the success rate. Comparing the success rate with the ones from previous years, it's shown that it decreases a little, although the drop is residual.

The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we reimplemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for scaling the network size that preserves first- and second-order statistics, building on existing work on network theory. Our new implementation enabled the use of more detailed neuron models with multicompartmental morphologies and multiple biophysically realistic ion channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, the relation to local field potentials, and other multiscale interactions. The scaling method we used provides flexibility to increase or decrease the network size as needed when running these CPU-intensive detailed simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient, large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures.

Neuronal avalanches and asynchronous irregular (AI) firing patterns have been thought to represent distinct frameworks to understand the brain spontaneous activity. The former is typically present in systems where there is a balance between the slow accumulation of tension and its fast dissipation, whereas the latter is accompanied by the balance between synaptic excitation and inhibition (E/I). Here, we develop a new theory of E/I balance that relies on two homeostatic adaptation mechanisms: the short-term depression of inhibition and the spike-dependent threshold increase. First, we turn off the adaptation and show that the so-called static system has a typical critical point commonly attributed to self-organized critical models. Then, we turn on the adaptation and show that the network evolves to a dynamic regime in which: (I) E/I synapses balance regardless of any parameter choice; (II) an AI firing pattern emerges; and (III) neuronal avalanches display power laws. This is the first time that these three phenomena appear simultaneously in the same network activity. Thus, we show that the once thought opposing frameworks may be unified into a single dynamics, provided that adaptation mechanisms are in place. In our model, the AI firing pattern is a direct consequence of the hovering close to the critical line where external inputs are compensated by threshold growth, creating synaptic balance for any E/I weight ratio.

Physicists are starting to work in areas where noisy signal analysis is required. In these fields, such as Economics, Neuroscience, and Physics, the notion of causality should be interpreted as a statistical measure. We introduce to the lay reader the Granger causality between two time series and illustrate ways of calculating it: a signal $X$ ``Granger-causes'' a signal $Y$ if the observation of the past of $X$ increases the predictability of the future of $Y$ when compared to the same prediction done with the past of $Y$ alone. In other words, for Granger causality between two quantities it suffices that information extracted from the past of one of them improves the forecast of the future of the other, even in the absence of any physical mechanism of interaction. We present derivations of the Granger causality measure in the time and frequency domains and give numerical examples using a non-parametric estimation method in the frequency domain. Parametric methods are addressed in the Appendix. We discuss the limitations and applications of this method and other alternatives to measure causality., Comment: 21 pages, 10 figures

The temporal dynamics of membrane voltage changes in neurons is controlled by ionic currents. These currents are characterized by two main properties: conductance and kinetics. The hyperpolarization-activated current ($I_{\rm h}$) strongly modulates subthreshold potential changes by shortening the excitatory postsynaptic potentials and decreasing their temporal summation. Whereas the shortening of the synaptic potentials caused by the $I_{\rm h}$ conductance is well understood, the role of the $I_{\rm h}$ kinetics remains unclear. Here, we use a model of the $I_{\rm h}$ current model with either fast or slow kinetics to determine its influence on the membrane time constant ($\tau_m$) of a CA1 pyramidal cell model. Our simulation results show that the $I_{\rm h}$ with fast kinetics decreases $\tau_m$ and attenuates and shortens the excitatory postsynaptic potentials more than the slow $I_{\rm h}$. We conclude that the $I_{\rm h}$ activation kinetics is able to modulate $\tau_m$ and the temporal properties of excitatory postsynaptic potentials (EPSPs) in CA1 pyramidal cells. In order to elucidate the mechanisms by which $I_{\rm h}$ kinetics controls $\tau_m$, we propose a new concept called "time scaling factor". Our main finding is that the $I_{\rm h}$ kinetics influences $\tau_m$ by modulating the contribution of the $I_{\rm h}$ derivative conductance to $\tau_m$., Comment: 15 pages, 7 figures

The field of computational modeling of the brain is advancing so rapidly that now it is possible to model large scale networks representing different brain regions with a high level of biological detail in terms of numbers and synapses. For a theoretician approaching a neurobiological question, it is important to analyze the pros and cons of each of the models available. Here, we provide a tutorial review on recent models for different brain circuits, which are based on experimentally obtained connectivity maps. We discuss particularities that may be relevant to the modeler when choosing one of the reviewed models. The objective of this review is to give the reader a fair notion of the computational models covered, with emphasis on the corresponding connectivity maps, and how to use them., Comment: 35 pages, 5 figures

O córtex cerebral apresenta padrões específicos de atividade espontânea produzidos endogenamente. Esses estados são caracterizados com relação ao grau de sincronia da atividade coletiva dos neurônios e ao nível de irregularidade dos disparos de potenciais de ação dos neurônios individuais. Um problema colocado à neurociência teórica é como modelar a emergência de estados espontâneos síncronos e assíncronos a partir de uma mesma rede de neurônios de disparos. Neste artigo, três modelos que oferecem solução para esse problema são revistos. Os modelos utilizam neurônios de disparo da classe conhecida como integra-e-dispara e consideram diferentes estruturas de rede e dinâmicas sinápticas. Mecanismos adotados pelos modelos, como balanço entre excitação e inibição e adaptação dependente dos disparos, são discutidos e contextualizados. Em paralelo, este artigo apresenta alguns conceitos utilizados na modelagem de neurônios e sinapses, oferecendo uma rápida introdução à neurociência teórica.

Replication

This special issue presents a series of 33 contributions in the area of dynamical networks and their applications. Part of the contributions is devoted to theoretical and methodological aspects of dynamical networks, such as collective dynamics of excitable systems, spreading processes, coarsening, synchronization, delayed interactions, and others. A particular focus is placed on applications to neuroscience and Earth science, especially functional climate networks. Among the highlights, various methods for dealing with noise and stochastic processes in neuroscience are presented. A method for constructing weighted networks with arbitrary topologies from a single dynamical node with delayed feedback is introduced. Also, a generalization of the concept of geodesic distances, a path-integral formulation of network-based measures is developed, which provides fundamental insights into the dynamics of disease transmission. The contributions from the Earth science application field substantiate predictive power of climate networks to study challenging Earth processes and phenomena.

Neuronal avalanches and asynchronous irregular (AI) firing patterns have been thought to represent distinct frameworks to understand the brain spontaneous activity. The former is typically present in systems where there is a balance between the slow accumulation of tension and its fast dissipation, whereas the latter is accompanied by the balance between synaptic excitation and inhibition (E/I). Here, we develop a new theory of E/I balance that relies on two homeostatic adaptation mechanisms: the short-term depression of inhibition and the spike-dependent threshold increase. First, we turn off the adaptation and show that the so-called static system has a typical critical point commonly attributed to self-organized critical models. Then, we turn on the adaptation and show that the network evolves to a dynamic regime in which: (I) E/I synapses balance regardless of any parameter choice; (II) an AI firing pattern emerges; and (III) neuronal avalanches display power laws. This is the first time that these three phenomena appear simultaneously in the same network activity. Thus, we show that the once thought opposing frameworks may be unified into a single dynamics, provided that adaptation mechanisms are in place. In our model, the AI firing pattern is a direct consequence of the hovering close to the critical line where external inputs are compensated by threshold growth, creating synaptic balance for any E/I weight ratio.HighlightsAsynchronous irregular (AI) firing happens together with power-law neuronal avalanches under self-organized synaptic balance.Self-organization towards the critical and balanced state (with AI and power-law avalanches) occur via short-term inhibition depression and firing threshold adaptation.The avalanche exponents match experimental findings.The adaptation time scales drive the self-organized dynamics towards different firing regimes.Author summaryTwo competing frameworks are employed to understand the brain spontaneous activity, both of which are backed by computational and experimental evidence: globally asynchronous and locally irregular (AI) activity arises in excitatory/inhibitory balanced networks subjected to external stimuli, whereas avalanche activity emerge in excitable systems on the critical point between active and inactive states. Here, we develop a new theory for E/I networks and show that there is a state where synaptic balance coexists with AI firing and power-law distributed neuronal avalanches. This regime is achieved through the introducing of short-term depression of inhibitory synapses and spike-dependent threshold adaptation. Thus, the system self-organizes towards the balance point, such that its AI activity arises from quasicritical fluctuations. The need for two independent adaptive mechanisms explains why different dynamical states are observed in the brain.

A produção de batatas foi introduzida no Brasil primeiramente pelo Sul do país onde as condições climáticas eram mais favoráveis para sua produção. A partir do início do século 21, a produção se expandiu para São Paulo e Minas Gerais. A batata é um tubérculo comestível, rico em amido e carboidratos, sendo relevante para a economia brasileira e sua produtividade no país está crescendo. Este trabalho teve como objetivo apresentar uma solução para os pequenos produtores que desejam investir em melhorar as condições de recolhimento de batata, tendo como principal foco o aumento de eficiência de 13,3% do que o produtor gasta hoje com mão de obra e aumento de produtividade do trabalho na lavoura de 33% em relação ao tempo atual, além da melhoria da saúde e redução da penosidade do trabalhador de campo. Para o desenvolvimento do protótipo foram levantados dados de campo de cultura do tubérculo. A carga no protótipo foi testada usando simulação de tensões aplicadas nos componentes. Esta máquina tem mecanismos que facilitam o processo de recolhimento de batatas no campo. A implementação do equipamento visou diminuir o risco ergométrico postural dos trabalhadores desta atividade.

Calcium-calmodulin dependent protein kinase II (CaMKII) regulates many forms of synaptic plasticity, but little is known about its functional role during plasticity induction in the cerebellum. Experiments have indicated that the β isoform of CaMKII controls the bidirectional inversion of plasticity at parallel fibre (PF)-Purkinje cell (PC) synapses in cerebellar cortex. Because the cellular events that underlie these experimental findings are still poorly understood, we developed a simple computational model to investigate how β CaMKII regulates the direction of plasticity in cerebellar PCs. We present the first model of AMPA receptor phosphorylation that simulates the induction of long-term depression (LTD) and potentiation (LTP) at the PF-PC synapse. Our simulation results suggest that the balance of CaMKII-mediated phosphorylation and protein phosphatase 2B (PP2B)-mediated dephosphorylation of AMPA receptors can determine whether LTD or LTP occurs in cerebellar PCs. The model replicates experimental observations that indicate that β CaMKII controls the direction of plasticity at PF-PC synapses, and demonstrates that the binding of filamentous actin to CaMKII can enable the β isoform of the kinase to regulate bidirectional plasticity at these synapses.

Wistar Audiogenic Rats (WARs) are genetically susceptible to sound-induced seizures that start in the brainstem and, in response to repetitive stimulation, spread to limbic areas, such as hippocampus. Analysis of the distribution of interevent intervals of GABAergic inhibitory postsynaptic currents (IPSCs) in CA1 pyramidal cells showed a monoexponential trend in Wistar rats, suggestive of a homogeneous population of synapses, but a biexponential trend in WARs. Based on this, we hypothesize that there are two populations of GABAergic synaptic release sites in CA1 pyramidal neurons from WARs. To address this hypothesis, we used a well-established neuronal computational model of a CA1 pyramidal neuron previously developed to replicate physiological properties of these cells. Our simulations replicated the biexponential trend only when we decreased the release frequency of synaptic currents by a factor of six in at least 40% of distal synapses. Our results suggest that almost half of the GABAergic synapses of WARs have a drastically reduced spontaneous release frequency. The computational model was able to reproduce the temporal dynamics of GABAergic inhibition that could underlie susceptibility to the spread of seizures.

Graves’ disease (GD) arising after the treatment of toxic multinodular goitre (TMNG) with radioiodine has long been described but it remained unclear whether GD was in fact iodine induced, its incidence, risk factors, natural history and treatment outcomes. Methods: A systematic search using The Cochrane Library, Medline and PubMed Central allowed the pooling of data from 3633 patients with thyroid autonomy, 1340 patients with TMNG, to fill gaps in knowledge, regarding the clinical expression of iodine-induced GD (131I-IGD) in adults. 131I-IGD developed in 0–5.3% of those with thyroid autonomy (first year) and in 5–5.4% of those with TMNG, 3–6 months after treatment. Patients with toxic adenoma were less affected. 131I-IGD was more common in patients with pre-treatment direct or indirect signs of autoimmunity: positive anti-TPO (p

Neuronal subthreshold voltage-dependent currents determine membrane properties such as the input resistance (Rin) and the membrane time constant (τm) in the subthreshold range. In contrast with classical cable theory predictions, the persistent sodium current (INaP), a non-inactivating mode of the voltage-dependent sodium current, paradoxically increases Rin and τm when activated. Furthermore, this current amplifies and prolongs synaptic currents in the subthreshold range. Here, using a computational neuronal model, we showed that the creation of a region of negative slope conductance by INaP activation is responsible for these effects and the ability of the negative slope conductance to amplify and prolong Rin and τm relies on the fast activation of INaP. Using dynamic clamp in hippocampal CA1 pyramidal neurons in brain slices, we showed that the effects of INaP on Rin and τm can be recovered by applying an artificial INaP after blocking endogenous INaP with tetrodotoxin. Furthermore, we showed that injection of a pure negative conductance is enough to reproduce the effects of INaP on Rin and τm and is also able to prolong artificial excitatory post synaptic currents. Since both the negative slope conductance and the almost instantaneous activation are critical for producing these effects, the INaP is an ideal current for boosting the amplitude and duration of excitatory post synaptic currents near the action potential threshold.

The environmental concerns are having a significant impact on the operation of power systems. The traditional Unit Commitment problem (UCP), which minimizes the total production costs is inadequate when environmental emissions need to be considered in the operation of power plants. This paper proposes a metaheuristic approach combined with a non-dominated sorting procedure to find solutions for the multi-objective UCP. The metaheuristic proposed, a Biased Random Key Genetic Algorithm, is a variant of the random-key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy.

This paper is an extension of work originally presented at the 2nd International Conference of the Portuguese Society for Engineering Education and aims to describe an interdisciplinarity teaching experiment involving three subjects of the scientific area of Mathematics and a fourth one in the area of Management. Using only one project, the students developed skills, in an integrated way, in the fields of the subjects involved. The structure of the project is described in detail. It is shown how the knowledge obtained in the different subjects is needed and how it connects together to answer the proposed challenges. We report the progress of the students’ work, the main difficulties and the skills developed during this process. We conclude with a reflection on the main problems and gains that may arise in similar projects.

The southwestern margin of Iberian (SWIM) marks the transition between the Mediterranean Alpine Orogenic Belt and the Atlantic Azores–Gibraltar Fracture Zone, near the diffuse segment of the Africa (Nubia)-Eurasia (Iberia) plate boundary. The Gulf of Cadiz Contourite System (GCCS) has been build-up by the circulation of the Mediterranean Outflow Water (MOW) on the continental middle slope. This work aims to understand how the tectonic structures controlled the development, evolution and morphology of the GCCS. This has been accomplished with the analysis of high quality regional 2D seismic reflection profiles. Four sedimentary basins were mapped in the study area – the Algarve, Doñana, Sanlucar and Cadiz basins – developed in the foreland of the Betic-Rif Orogen. Three major tectonic structures – the Gil Eanes Fault (GEF), Cadiz Fault (CF) and the Albufeira-Guadalquivir-Doñana Basement High (AGDBH) – were identified on the SWIM. The NW-SE-oriented GEF and the NE-SW to ENE-WSW-oriented CF were identified as dextral strike-slip faults. The AGD is an E-W to ENE-WSW elongated morphostructural high that marks the southern boundary of the Algarve Basin. Based on their location and orientation they were interpreted as being inherited structures from the Mesozoic rift system. Based on the described regional structures, the SWIM was divided into four tectonic domains (A, B, C and D) with different structural and seismological characteristics. Contourite depositional and erosional features show different characteristics – distinct size, extension, configuration and depositional architecture - for each of the tectonic domains recognised. Tectonic-controlled subsidence led to the development of an accommodation space, forming the main depositional sector in the GCCS (Domain C). Contrariwise, where the margin suffered uplift, the accommodation space was limited and the contourite depositional features are not very extensive (Domain D). The presence of structural obstacles (e.g. AGDBH, paleo-slope) is another important factor in the drift evolution: mounded geometries were only observed where important structural obstacles conditioned the current circulation (Domain B, C and D). Where the seafloor is gentle with smooth relief, spread-out MOW circulation occurs, forming sheeted drifts related to weak and wide non-focused bottom-currents (Domain A). This work demonstrates the influence that the inherited tectonic structures and the margin paleo-topography has on the development of the contourite system. Furthermore, we propose that tectonics also control the dimensions and types of the contourite depositional features.Acknowledgements: D.D. thanks the Portuguese Foundation for Science and Technology (FCT) for a PhD scholarship (reference SFRH/BD/115962/2016). This research has been conducted under the framework of ‘The Drifters Research Group’, Department of Earth Sciences, Royal Holloway University of London (UK). This project is partially funded by a Joint Industry Project supported by TOTAL, BP, ENI, ExxonMobil, TGS and Wintershall and partially supported through the CGL2016-80445-R (AEI/FEDER, UE), CGL2015-66835-P and CTM2016-75129-C3-1-R.

Self-sustained activity in the brain is observed in the absence of external stimuli and contributes to signal propagation, neural coding, and dynamic stability. It also plays an important role in cognitive processes. In this work, by means of studying intracellular recordings from CA1 neurons in rats and results from numerical simulations, we demonstrate that self-sustained activity presents high variability of patterns, such as low neural firing rates and activity in the form of small-bursts in distinct neurons. In our numerical simulations, we consider random networks composed of coupled, adaptive exponential integrate-and-fire neurons. The neural dynamics in the random networks simulates regular spiking (excitatory) and fast spiking (inhibitory) neurons. We show that both the connection probability and network size are fundamental properties that give rise to self-sustained activity in qualitative agreement with our experimental results. Finally, we provide a more detailed description of self-sustained activity in terms of lifetime distributions, synaptic conductances, and synaptic currents.

Epilepsy has been a central topic in computational neuroscience, and in silico models have shown to be excellent tools to integrate and evaluate findings from animal and clinical settings. Among the different languages and tools for computational modeling development, NEURON stands out as one of the most used and mature neurosimulators. However, despite the vast quantity of models developed with NEURON, a fragmentation problem is evident in the great majority of models related to the same type of cell or cell properties. This fragmentation causes a lack of interoperability between the models because of differences in parameters. The problem is not related to the neurosimulator, which is prepared to reuse elements of other models, but related to decisions made during the model development, when it is not uncommon to adjust parameter values according to the necessities of the study. Here, this problem is presented by studying computational models related to temporal lobe epilepsy and the definitions of hippocampal CA1 pyramidal cells. The current assessment aims to highlight the implications of fragmentation for reliable modeling and the need to adopt a framework that allows a better interoperability between different models. This article is part of the Special Issue “NEWroscience 2018”.

The operation of a novel large area micro-patterned gaseous electron multiplier, made from a 125 micron thick copper claded kapton foil, the COBRA_125, is presented. The COBRA_125 is equiped with 3 independent electrodes which allow to establish 2 independent multiplication regions in a single micro-patterened gaseous electron mutiplier. We report on the operation of a COBRA_125 with an active area of 100×100 mm2. Charge gains above 104 and energy resolutions in the range 18%–20% were achieved in a mixture of Ar-CH4 (90%–10%) by irradiation with X-rays from 55Fe source. Gain and energy resolutions were stable over the detector area, with maximum deviation from the average values of 8% and 15%, respectively.

Calcium-calmodulin dependent protein kinase II (CaMKII) regulates many forms of synaptic plasticity, but little is known about its functional role during plasticity induction in the cerebellum. Experiments have indicated that the β isoform of CaMKII controls the bidirectional inversion of plasticity at parallel fibre (PF)-Purkinje cell (PC) synapses in cerebellar cortex. Because the cellular events that underlie these experimental findings are still poorly understood, we developed a simple computational model to investigate how βCaMKII regulates the direction of plasticity in cerebellar PCs. We present the first model of AMPA receptor phosphorylation that simulates the induction of long-term depression (LTD) and potentiation (LTP) at the PF-PC synapse. Our simulation results suggest that the balance of CaMKII-mediated phosphorylation and protein phosphatase 2B (PP2B)-mediated dephosphorylation of AMPA receptors can determine whether LTD or LTP occurs in cerebellar PCs. The model replicates experimental observations that indicate that βCaMKII controls the direction of plasticity at PF-PC synapses, and demonstrates that the binding of filamentous actin to CaMKII can enable the β isoform of the kinase to regulate bidirectional plasticity at these synapses.Author SummaryMany molecules and the complex interactions between them are involved in synaptic plasticity in the cerebellum. However, the exact relationship between cerebellar plasticity and the different signalling cascades remains unclear. Calcium-calmodulin dependent protein kinase II (CaMKII) is an important component of the signalling network that is responsible for plasticity in cerebellar Purkinje cells (PCs). The CaMKII holoenzyme contains different isoforms such as αCaMKII and βCaMKII. Experiments with Camk2b knockout mice that lack the β isoform of CaMKII demonstrated that βCaMKII regulates the direction of plasticity at parallel fibre (PF)-PC synapses. Stimulation protocols that induce long-term depression in wild-type mice, which contain both α and βCaMKII, lead to long-term potentiation in knockout mice without βCaMKII, and vice versa. We developed a kinetic simulation of the phosphorylation and dephosphorylation of AMPA receptors by CaMKII and protein phosphatase 2B to investigate how βCaMKII can control bidirectional synaptic plasticity in cerebellar PCs. Our simulation results demonstrate that the binding of filamentous actin to βCaMKII can contribute to the regulation of bidirectional plasticity at PF-PC synapses. Our computational model of intracellular signalling significantly advances the understanding of the mechanisms of synaptic plasticity induction in the cerebellum.

New findings What is the central question of this study? After sino-aortic denervation (SAD), rats present normal levels of mean arterial pressure (MAP), high MAP variability and changes in breathing. However, mechanisms involved in SAD-induced respiratory changes and their impact on the modulation of sympathetic activity remain unclear. Herein, we characterized the firing frequency of medullary respiratory neurons after SAD. What is the main finding and its importance? Sino-aortic denervation-induced prolonged inspiration was associated with a reduced interburst frequency of pre-inspiratory/inspiratory neurons and an increased long-term variability of late inspiratory neurons, but no changes were observed in the ramp-inspiratory and post-inspiratory neurons. This imbalance in the respiratory network might contribute to the modulation of sympathetic activity after SAD. Abstract In previous studies, we documented that after sino-aortic denervation (SAD) in rats there are significant changes in the breathing pattern, but no significant changes in sympathetic activity and mean arterial pressure compared with sham-operated rats. However, the neural mechanisms involved in the respiratory changes after SAD and the extent to which they might contribute to the observed normal sympathetic activity and mean arterial pressure remain unclear. Here, we hypothesized that after SAD, rats present with changes in the firing frequency of the ventral medullary inspiratory and post-inspiratory neurons. To test this hypothesis, male Wistar rats underwent SAD or sham surgery and 3 days later were surgically prepared for an in situ experiment. The duration of inspiration significantly increased in SAD rats. During inspiration, the total firing frequency of ramp-inspiratory, pre-inspiratory/inspiratory and late-inspiratory neurons was not different between groups. During post-inspiration, the total firing frequency of post-inspiratory neurons was also not different between groups. Furthermore, the data demonstrate a reduced interburst frequency of pre-inspiratory/inspiratory neurons and an increased long-term variability of late-inspiratory neurons in SAD compared with sham-operated rats. These findings indicate that the SAD-induced prolongation of inspiration was not accompanied by alterations in the total firing frequency of the ventral medullary respiratory neurons, but it was associated with changes in the long-term variability of late-inspiratory neurons. We suggest that the timing imbalance in the respiratory network in SAD rats might contribute to the modulation of presympathetic neurons after removal of baroreceptor afferents.

Resumo Neurônios respondem a estímulos externos emitindo sequências de potenciais de ação (trens de disparos). Desse modo, pode-se dizer que o trem de disparos é a resposta neuronal a um estímulo de entrada. Potenciais de ação são fenômenos do tipo “tudo ou nada”, isto é, um trem de disparos pode ser representado por uma sequência de zeros e uns. No contexto da teoria da informação, pode-se então questionar: quanta informação acerca do estímulo externo o trem de disparos carrega? Ou ainda, quais aspectos do estímulo são codificados pela resposta neuronal? Neste artigo, faz-se uma introdução à teoria da informação em que são apresentados aspectos históricos, conceitos fundamentais da teoria e aplicações à neurociência. A conexão com a neurociência é feita com o uso de demonstrações e discussões de diferentes métodos da teoria da informação. Exemplos são fornecidos com o uso de simulações computacionais de dois modelos de neurônios, o neurônio Poisson e o neurônio integra-e-dispara, e um modelo de rede de autômatos celulares. No ultimo caso, demonstra-se como se pode utilizar medidas da teoria da informação para reconstruir a matriz de conectividade de uma rede. Todos os códigos utilizados para estas simulações foram disponibilizados publicamente na plataforma GitHub, acessíveis pelo url: github.com/ViniciusLima94/ticodigoneural.

Antipsychotic medication non-adherence is a complex and multifaceted problem that may hinder recovery in psychosis-spectrum disorders. Therefore, it warrants an early and comprehensive assessment. Current self-report measures focus entirely on behavioral and attitudinal barriers to adherence, failing to provide insight about key psychosocial drivers such as shame and stigma that may also account for non-adherence. This study's main goals were to develop a brief scale for measuring antipsychotic (non)-adherence and associated intra and interpersonal barriers (Antipsychotic Medication Beliefs and Attitudes Scale - AMBAS), and explore its psychometric properties. One hundred and seventy participants with a psychosis-spectrum disorder were recruited and filled in a battery of self-report measures. Exploratory factor analysis supported a two-factor solution, with one factor tapping the influence of different barriers to medication adherence and other factor encompassing perceived positive effects of medication. The scale presented good reliability and convergent validity as evidenced by significant moderate associations with the Medication Adherence Rating Scale. Although in need for further study, AMBAS seems a valid and reliable measure to assess antipsychotic (non)-adherence and underlying behavioral and psychosocial drivers. With replication, AMBAS might be a useful measure that could be used in different clinical and research settings.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

The expression of activation-induced cytidine deaminase, B-aggressive lymphoma, cyclin D1 and serine/threonine kinase 15 genes, among others, is increased in B cells from patients with chronic hepatitis C virus (HCV) infection. It is unknown whether the level of expression of these genes in B cells is increased in patients with hepatitis C who have achieved a sustained virological response (SVR) but who have persistent, detectable HCV RNA, so-called occult infection. Eighty-three patients who achieved and SVR, 27 with detectable HCV and 56 without detectable HCV RNA, 28 chronic hepatitis C patients and 32 healthy controls were studied. RNA was extracted from B cells, and gene expression levels were measured by RT-PCR. Patients with chronic HCV and those who achieved an SVR (with and without persistent low-level HCV RNA) showed a statistically significant higher expression compared to healthy controls, of activation-induced cytidine deaminase (P = 0.004, P < 0.001 and P = 0.002, respectively), B-aggressive lymphoma (P < 0.001, P = 0.001 and P = 0.006) and cyclin D1 (P = 0.026, P = 0.001; P = 0.038). For activation-induced cytidine deaminase patients with an SVR and 'occult infection' had a statistically significantly higher expression level than patients with and SVR without 'occult infection' (P = 0.014). The higher expression levels found for activation-induced cytidine deaminase, together with other genes indicates that these B lymphomagenesis-related genes are upregulated following HCV therapy and this is more marked when HCV can be detected in PBMCs.

A new weighted rank correlation coefficient r W 2 has been introduced in Pinto da Costa (2011), following the coefficient r W introduced in Pinto Da Costa and Soares (2005); Soares et al. (2001); Pinto Da Costa et al. (2001). We give the expression of r W 2 in the case of ties and also present some simulations to study the behaviour of the coefficient.