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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.

Finite element analysis was conducted to study the stress behaviour in a generic electric high-revolution rimdriven rotor in relation to the number of spokes and rotational speed using the maximum distortion energy failure criterion. The paper analysed the stresses on an electric high-revolution rotor and locate the failure’s potential origins. Upon analysing the FEA results, it is found that the maximum equivalent von-Mises stress is related to the square of revolution in RPM. It is concluded that it is possible to create a general formula that can predict the stresses, simplifying the development stage of an electric rim-driven rotor for high-speed applications.

We consider a farm of Kite Power Systems (KPS) in the field of Airborne Wind Energy (AWE), in which each kite is connected to an electric ground generator by a tether. In particular, we address the problem of selecting the best layout of such farm in a given land area such that the total electrical power generated is maximized. The kites, typically, fly at high altitudes, sweep a greater area than that of traditional wind turbines, and move within a conic shaped volume with vertex on the ground station. Therefore, constraints concerning kite collision avoidance and terrain boundaries must be considered. The efficient use of a given land area by a set of KPS depends on the location of each unit, on its tether length and on the elevation angle. In this work, we formulate the KPS farm layout optimization problem. Considering a specific KPS and wind characteristics of the given location, we study the power curve as a function of the tether length and elevation angle. Combining these results with an area with specified length and width, we develop and implement a heuristic optimization procedure to devise the layout of a KPS farm that maximizes wind power generation., We acknowledge the support of FEDER/COMPETE2020/NORTE2020/POCI/PIDDAC/MCTES/FCT, Portugal funds through grants SFRH/BPD/126683/2016, UID/IEEA/00147/006933–SYSTEC, PTDC/EEIAUT/2933/2014–TOCCATA, PTDC/EEI-AUT/31447/2017–UPWIND, POCI-01-0145-FEDER031821-FAST and NORTE-01-0145-FEDER-000033–STRIDE.

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.

The quest for lighter and shorter propulsion systems has led to the reduction of axial compressor and fan blade chord-lengths. Theoretical and experimental results show that the geometrical aspect-ratio criterion significantly affects the overall performance of axial flow compressors and fans. In conducting this review, it was found that the experimental results differ from one literature source to another. Highlighting that the way in which blade aspect ratio affects the performance of axial flow compressors and fans is still not fully understood. Nonetheless, the reviewed literature has still proven valuable for compressor and fan design. This paper focuses on creating a review of the available literature relating to how low aspect ratio blades may affect the performance of electrical axial compressors and fans.

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.

eLearning has been the medium of delivery of medical educational institutions to address the scarcity of medical professionals during the COVID-19 pandemic. In this study, the Unified Theory of Acceptance and Use of Technology (UTAUT2) was extended to determine the factors affecting the acceptance of eLearning platforms to medical education in the Philippines during the COVID-19 pandemic. A total of 360 medical students voluntary participated and answered an online questionnaire that consisted of 40 questions. Structural Equation Modeling (SEM) indicated that performance expectancy was found to have the highest effect on behavioral intention, which was followed by learning value and instructor characteristics. A high behavioral intention was found to affect the actual use of eLearning platforms. Interestingly, social influence and habit were found not to be significant to behavioral intentions. This study is the first study that has explored the acceptance of eLearning platforms among medical students in the Philippines during the COVID-19 pandemic. The findings can be a theoretical guideline of the Commission on Higher Education of the Philippines for eLearning platforms. Finally, the framework would be very valuable for enhancing the open innovation in eLearning platforms in medical fields worldwide.

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.

Neurons in the nervous system are submitted to distinct sources of noise, such as ionic-channel and synaptic noise, which introduces variability in their responses to repeated presentations of identical stimuli. This motivates the use of stochastic models to describe neuronal behavior. In this work, we characterize an intrinsically stochastic neuron model based on a voltage-dependent spike probability function. We determine the effect of the intrinsic noise in single neurons by measuring the spike time reliability and study the stochastic resonance phenomenon. The model was able to show increased reliability for non-zero intrinsic noise values, according to what is known from the literature, and the addition of intrinsic stochasticity in it enhanced the region in which stochastic resonance is present. We proceeded to the study at the network level where we investigated the behavior of a random network composed of stochastic neurons. In this case, the addition of an extra dimension, represented by the intrinsic noise, revealed dynamic states of the system that could not be found otherwise. Finally, we propose a method to estimate the spike probability curve from in vitro electrophysiological data., 15 pages, 5 figures

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