Miracle-dz/UniFAR-FTUs huggingface dataset:
Does left ventricular hypertrophy by electrocardiogram predict adverse outcomes in pregnancies with chronic hypertension?

Abstract Objectives: The American College of Obstetricians and Gynecologists Task Force on Hypertension in Pregnancy recommends assessing left ventricular function with echocardiogram or electrocardiogram (ECG) in women with severe hypertension of long duration. We aim to compare the rates of superimposed preeclampsia (SIP) and other obstetrical outcomes in pregnancies with chronic hypertension (CHTN) and left ventricular hypertrophy (LVH) by ECG criteria with mothers without LVH. Study design: After IRB approval, we performed a retrospective chart review from January 2015 to December 2016. Singleton pregnancies with CHTN who carried the pregnancy beyond 20 weeks and planned to deliver at our institution were included for analysis. Patients with insufficient data, multiple gestations, and those with systemic lupus erythematosus were excluded from this study. Rates of SIP and other obstetrical outcomes were compared among those with LVH and those without LVH. Univariate parametric and nonparametric statistical models were applied as appropriate. Associations were considered statistically significant at an alpha level of 0.05. Results: We included 218 pregnancies. Nineteen (8.7%) had LVH. The rate of SIP was higher in pregnancies with LVH than in those without it (68 versus 41%; OR = 3.01; 95% CI 1.1–8.5; p = .022). The birthweight was lower in the LVH group (2432 g [2120–2990] versus 2870 g [2430–3440]; p = .016). Other obstetrical outcomes were not significantly different between those patients with LVH and those without. Conclusions: LVH by ECG criteria may be associated with higher rates of SIP and with lower birthweight in pregnancies with CHTN. ECG may be a cost-effective tool to identify patients with CHTN at risk of adverse outcomes. Larger trials are necessary to corroborate our findings.
Validation of EGameFlow: A Self-Report Scale for Measuring User Experience in Video Game Play

The development of a relevant model for measuring user enjoyment of video game play has received a great deal of attention in game-based and flow-based literature. EGameFlow, a self-report scale instrument created from the original game enjoyment framework proposed by Sweetser and Wyeth, provides a necessary and potentially useful tool for game enjoyment researchers. However, the scale itself is quite new. The utility of EGameFlow cannot be determined until its rigorousness has been verified. The purpose of this study was to test the validity, reliability, and applicability of EGameFlow for measuring players’ experiences in video game play. A total of 167 participants played an interactive video game and then evaluated their game playing experiences via the refined 27-item EGameFlow scale, which included the following seven dimensions: concentration, goal clarity, feedback, challenge, autonomy, immersion, and social interaction. Confirmatory factor analysis, reliability testing, and discriminant validity checks were administered. Empirical results indicated that the refined scale was both valid and reliable. Implications of these findings and direction for future research were also discussed.
Fibre Bragg Grating Based Strain Sensors: Review of Technology and Applications

Fibre Bragg grating (FBG) strain sensors are not only a very well-established research field, but they are also acquiring a bigger market share due to their sensitivity and low costs. In this paper we review FBG strain sensors with high focus on the underlying physical principles, the interrogation, and the read-out techniques. Particular emphasis is given to recent advances in highly-performing, single head FBG, a category FBG strain sensors belong to. Different sensing schemes are described, including FBG strain sensors based on mode splitting. Their operation principle and performance are reported and compared with the conventional architectures. In conclusion, some advanced applications and key sectors the global fibre-optic strain sensors market are envisaged, as well as the main market players acting in this field.
SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours

Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the danger of “fake news” has become a mainstream concern. However automated support for rumour verification remains in its infancy. It is therefore important that a shared task in this area continues to provide a focus for effort, which is likely to increase. Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour’s veracity. As in RumourEval 2017 we provided a dataset of dubious posts and ensuing conversations in social media, annotated both for stance and veracity. The social media rumours stem from a variety of breaking news stories and the dataset is expanded to include Reddit as well as new Twitter posts. There were two concrete tasks; rumour stance prediction and rumour verification, which we present in detail along with results achieved by participants. We received 22 system submissions (a 70% increase from RumourEval 2017) many of which used state-of-the-art methodology to tackle the challenges involved.
Formal Barriers to Longest-Chain Proof-of-Stake Protocols

The security of most existing cryptocurrencies is based on a concept called Proof-of-Work, in which users must solve a computationally hard cryptopuzzle to authorize transactions ("one unit of computation, one vote''). This leads to enormous expenditure on hardware and electricity in order to collect the rewards associated with transaction authorization. Proof-of-Stake is an alternative concept that instead selects users to authorize transactions proportional to their wealth ("one coin, one vote"). Some aspects of the two paradigms are the same. For instance, obtaining voting power in Proof-of-Stake has a monetary cost just as in Proof-of-Work: a coin cannot be freely duplicated any more easily than a unit of computation. However some aspects are fundamentally different. In particular, exactly because Proof-of-Stake is wasteless, there is no inherent resource cost to deviating (commonly referred to as the "Nothing-at-Stake'' problem). In contrast to prior work, we focus on incentive-driven deviations (any participant will deviate if doing so yields higher revenue) instead of adversarial corruption (an adversary may take over a significant fraction of the network, but the remaining players follow the protocol). The main results of this paper are several formal barriers to designing incentive-compatible proof-of-stake cryptocurrencies (that don't apply to proof-of-work).
Node classification for signed networks using diffuse interface methods

Signed networks are a crucial tool when modeling friend and foe relationships. In contrast to classical undirected, weighted graphs, the edge weights for signed graphs are positive and negative. Crucial network properties are often derived from the study of the associated graph Laplacians. We here study several different signed network Laplacians with a focus on the task of classifying the nodes of the graph. We here extend a recently introduced technique based on a partial differential equation defined on the signed network, namely the Allen-Cahn-equation, to classify the nodes into two or more classes. We illustrate the performance of this approach on several real-world networks.
Peroxisome Proliferator Activated Receptor Agonists Modulate Transposable Element Expression in Brain and Liver

Peroxisome proliferator activated receptors (PPARs) are nuclear hormone receptors that act as transcription factors in response to endogenous lipid messengers. The fibrates and thiazolidinediones are synthetic PPAR agonists used clinically to treat dyslipidemia and Type 2 Diabetes Mellitus, respectively, but also improve symptoms of several other diseases. Transposable elements (TEs), repetitive sequences in mammalian genomes, are implicated in many of the same conditions for which PPAR agonists are therapeutic, including neurodegeneration, schizophrenia, and drug addiction. We tested the hypothesis that there is a link between actions of PPAR agonists and TE expression. We developed an innovative application of microarray data by mapping Illumina mouse WG-6 microarray probes to areas of the mouse genome that contain TEs. Using this information, we assessed the effects of systemic administration of three PPAR agonists with different PPAR subtype selectivity: fenofibrate, tesaglitazar, and bezafibrate, on TE probe expression in mouse brain [prefrontal cortex (PFC) and amygdala] and liver. We found that fenofibrate, and bezafibrate to a lesser extent, up-regulated probes mapped to retrotransposons: Short-Interspersed Elements (SINEs) and Long-Interspersed Elements (LINEs), in the PFC. Conversely, all PPAR agonists down-regulated LINEs and tesaglitazar and bezafibrate also down-regulated SINEs in liver. We built gene coexpression networks that partitioned the diverse transcriptional response to PPAR agonists into groups of probes with highly correlated expression patterns (modules). Most of the differentially expressed retrotransposons were within the same module, suggesting coordinated regulation of their expression, possibly by PPAR signaling. One TE module was conserved across tissues and was enriched with genes whose products participate in epigenetic regulation, suggesting that PPAR agonists affect TE expression via epigenetic mechanisms. Other enriched functional categories included phenotypes related to embryonic development and learning and memory, suggesting functional links between these biological processes and TE expression. In summary, these findings suggest mechanistic relationships between retrotransposons and PPAR agonists and provide a basis for future exploration of their functional roles in brain and liver.
Statistical Analysis of Spectral Entropy Features for the Detection of Alcoholics Based on Elecroencephalogram (EEG) Signals

EEG happens to be an important tool for brain study providing a non- invasive and cost effective method to detect the effects of alcohol on the human brain. This paper highlights the importance of statistical analysis to determine the usefulness of spectral entropy features for discriminating alcoholics from healthy subjects. The open source EEG database consisting of 10 alcoholic and 10 control subjects recordings under visual stimulus is considered for the study. The EEG signal is preprocessed to remove eye blink artefact using independent component analysis (ICA) and the gamma sub band is extracted by using an elliptic band pass filter to obtain the visually evoked pattern (VEP) signal. The spectral entropy (SEN) coefficients are computed on all the 61 VEP signals of each subject. To obtain a statistical measure of SEN coefficients suitability for classifying the alcoholic EEG, ANOVA tests are performed. Results show that the test exhibits interesting observations in the form of p-value 0.05 for the remaining channels. This study may help in identifying those significant channels (p<0.05) which contribute to the classification of both the groups.
A single dose of beetroot juice improves endothelial function but not tissue oxygenation in pregnant women: a randomised clinical trial

Abstract Beetroot juice (BJ) consumption has been associated with improved cardiovascular health owing to an increase in NO bioconversion. This study evaluates the effect of BJ consumption on macrovascular endothelial function (flow-mediated dilation (FMD)) and muscle oxygen saturation (StO2) parameters in pregnant women within a randomised, crossover, double-blind design in which twelve pregnant women consumed a single dose (140 ml) of BJ or placebo (PLA). Urinary nitrate was assessed before (T0) and 150 min after BJ/PLA consumption. FMD was used to evaluate macrovascular endothelial function, and near-IR spectroscopy was used to evaluate muscle StO2 parameters during the occlusion and reperfusion phases, which were taken at baseline (PRE) and 120 and 140 min after BJ/PLA consumption, respectively. A significant increase in urinary nitrate was observed at 150 min after BJ consumption when compared with T0 (BJ: 0·20 (sd 0·13) v. T0: 0·02 (sd 0·00), P=0·000) and PLA intervention (PLA: 0·02 (sd 0·00), P=0·001). FMD improved after BJ consumption when compared with PRE (BJ: 11·00 (sd 1·67) v. PRE: 5·53 (sd 1·17), P=0·000) and PLA (5·34 (sd 1·31), P=0·000). No significant difference between PLA and PRE in FMD (P=1·000) was observed. In StO2 parameters, a difference was not observed after BJ consumption compared with PRE and PLA intervention. The data demonstrate that a single dose of 140 ml of BJ consumption improves macrovascular endothelial function, but not StO2 parameters.
Hydrogen Sulfide Mediates K+ and Na+ Homeostasis in the Roots of Salt-Resistant and Salt-Sensitive Poplar Species Subjected to NaCl Stress

Non-invasive micro-test techniques (NMT) were used to analyze NaCl-altered flux profiles of K+, Na+, and H+ in roots and effects of NaHS (a H2S donor) on root ion fluxes in two contrasting poplar species, Populus euphratica (salt-resistant) and Populus popularis (salt-sensitive). Both poplar species displayed a net K+ efflux after exposure to salt shock (100 mM NaCl), as well as after short-term (24 h), and long-term (LT) (5 days) saline treatment (50 mM NaCl, referred to as salt stress). NaHS (50 μM) restricted NaCl-induced K+ efflux in roots irrespective of the duration of salt exposure, but K+ efflux was not pronounced in data collected from the LT salt stress treatment of P. euphratica. The NaCl-induced K+ efflux was inhibited by a K+ channel blocker, tetraethylammonium chloride (TEA) in P. popularis root samples, but K+ loss increased with a specific inhibitor of plasma membrane (PM) H+-ATPase, sodium orthovanadate, in both poplar species under LT salt stress and NaHS treatment. This indicates that NaCl-induced K+ loss was through depolarization-activated K+ channels. NaHS caused increased Na+ efflux and a corresponding increase in H+ influx for poplar roots subjected to both the short- and LT salt stress. The NaHS-enhanced H+ influx was not significant in P. euphratica samples subjected to short term salt stress. Both sodium orthovanadate and amiloride (a Na+/H+ antiporter inhibitor) effectively inhibited the NaHS-augmented Na+ efflux, indicating that the H2S-enhanced Na+ efflux was due to active Na+ exclusion across the PM. We therefore conclude that the beneficial effects of H2S probably arise from upward regulation of the Na+/H+ antiport system (H+ pumps and Na+/H+ antiporters), which promote exchange of Na+ with H+ across the PM and simultaneously restricted the channel-mediated K+ loss that activated by membrane depolarization.
Tomato Plant Diseases Classification Using Statistical Texture Feature and Color Feature

Plant disease classification has been associated with the production of essential food crops and human society. In this paper, we classify tomato plant disease using two different features: texture and color. For a texture feature, we extract statistical texture information (shape, scale and location) of an image from Scale invariant Feature Transform (SIFT) feature. As a main contribution, a new approach is introduced to model the Scale Invariant Feature Transform (SIFT) texture feature by Johnson SB distribution for statistical texture information of an image. The moment method is used to estimate the parameters of Johnson SB distribution. The mathematical representation of SIFT feature is matrix representation and too complex to be applied in image classification. Therefore, we propose a new statistical feature to represent the image in few numbers of dimensions. For a color feature, we extract statistical color information of an image from RGB color channel. The color statistics feature is the combination of mean, standard deviation and moments from degree three to five for each RGB color channel. Our proposed feature is a combination of statistical texture and color features to classify tomato plant disease. The experimental performance on PlantVillage database is compared with state-of-art feature vectors to highlight the advantages of the proposed feature.
Relative Yield of MEG and EEG Spikes in Simultaneous Recordings

Purpose: Most clinical magnetoencephalography (MEG) centers record both MEG and EEG, but model only MEG sources. This may be related to the belief that MEG spikes are more prevalent, MEG is more sensitive, or to proprietary software limitations. Biophysics would contend, however, that EEG, being sensitive to radial and tangential source orientations, would provide complementary data for analysis. Methods: We recorded 306 channels of MEG and 25 channels of EEG simultaneously in 297 consecutive patients over 3 years. We inspected the MEG and EEG recordings separately, identified spikes in both, determined whether their voltage and/or magnetometer magnetic fields were dipolar and thus model-worthy, and segregated them into types based on similar and distinct field topography. We placed for each patient their spike types into categories, including those with both a recognizable MEG and EEG signal and those with only an MEG and only an EEG signal. Results: Eighty-three percent of patients had spikes recorded, and these patients had an average of 2.7 spike types each. Fifty-six percent of spike types were present in both MEG and EEG. However, 36% of spike types were only evident in EEG, whereas 8% were noted in MEG alone. In 49% of patients with spikes, MEG review missed at least one spike type, whereas in 17% of patients, EEG review missed at least one spike type. Conclusions: To obtain an optimal yield of diagnostic information, EEG should also be subjected to source analysis in any clinical MEG study. EEG and MEG data are indeed complementary.
Alternatives to patient specific verification measurements in proton therapy: a comparative experimental study with intentional errors

Patient specific verification (PSV) measurements for pencil beam scanning (PBS) proton therapy are resource-consuming and necessitate substantial beam time outside of clinical hours. As such, efforts to safely reduce the PSV-bottleneck in the clinical work-flow are of great interest. Here, capabilities of current PSV methods to ensure the treatment integrity were investigated and compared to an alternative approach of reconstructing the dose distribution directly from the machine control- or delivery log files with the help of an independent dose calculation (IDC). Scenarios representing a wide range of delivery or work-flow failures were identified (e.g. error in spot position, air gap or pre-absorber setting) and machine files were altered accordingly. This yielded 21 corrupted treatment files, which were delivered and measured with our clinical PSV protocol. IDC machine- and log file checks were also conducted and their sensitivity at detecting the errors compared to the measurements. Although some of the failure scenarios induced clinically relevant dose deviations in the patient geometry, the PSV measurement protocol only detected one out of 21 error scenarios. However, 11 and all 21 error scenarios were detected using dose reconstructions based on the log and machine files respectively. Our data suggests that, although commonly used in particle therapy centers, PSV measurements do a poor job detecting data transfer failures and imperfect delivery machine performance. Machine- and log-file IDCs have been shown to successfully detect erroneous work-flows and to represent a reliable addition to the QA procedure, with the potential to replace PSV.
Cannabinoids and spinal cord stimulation for the treatment of failed back surgery syndrome refractory pain

Objective This study aimed to evaluate pain and its symptoms in patients with failed back surgery syndrome (FBSS) refractory to other therapies, treated with a combination of delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), in association with spinal cord stimulation (SCS). Settings Outpatients referred at Pain Unit of San Vincenzo Hospital in Taormina (Italy), between September 2014 and January 2016. Subjects Eleven FBSS patients diagnosed with neuropathic pain using the Douleur Neuropathique 4 questionnaire and suffering from moderate to severe chronic refractory pain, and undergoing treatment with SCS and a combination of THC/CBD for 12 consecutive months. Materials and methods All the included patients discontinued previous unsuccessful therapy at least 2 months before the beginning of the cannabinoid therapy, with the exception of the SCS that was continued. Patients received a fixed dosage of cannabinoid agonists (THC/CBD) that could be increased subjective to pain control response. A Brief Pain Inventory questionnaire was administered to measure pain and its interference with characteristic dimensions of feelings and functions. The duration of treatment with SCS and THC/CBD combination was 12 months. Results Effective pain management as compared to baseline result was achieved in all the cases studied. The positive effect of cannabinoid agonists on refractory pain was maintained during the entire duration of treatment with minimal dosage titration. Pain perception, evaluated through numeric rating scale, decreased from a baseline mean value of 8.18±1.07–4.72±0.9 by the end of the study duration (12 months) (P<0.001). Conclusion The results indicate that cannabinoid agonists (THC/CBD) can have remarkable analgesic capabilities, as adjuvant of SCS, for the treatment of chronic refractory pain of FBSS patients.
Perturbations of minimizing movements and curves of maximal slope

We modify the De Giorgi's minimizing movements scheme for a functional $φ$, by perturbing the dissipation term, and find a condition on the perturbations which ensures the convergence of the scheme to an absolutely continuous perturbed minimizing movement. The perturbations produce a variation of the metric derivative of the minimizing movement. This process is formalized by the introduction of the notion of curve of maximal slope for $φ$ with a given rate. We show that if we relax the condition on the perturbations we may have many different meaningful effects; in particular, some perturbed minimizing movements may explore different potential wells.
Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market?

Unpredictable stock market factors make it difficult to predict stock index futures. Although efforts to develop an effective prediction method have a long history, recent developments in artificial intelligence and the use of artificial neural networks have increased our success in nonlinear approximation. When we study financial markets, we can now extract features from a big data environment without prior predictive information. We here propose to further improve this predictive performance using a combination of a deep-learning-based stock index futures prediction model, an autoencoder, and a restricted Boltzmann machine. We use high-frequency data to examine the predictive performance of deep learning, and we compare three traditional artificial neural networks: 1) the back propagation neural network; 2) the extreme learning machine; and 3) the radial basis function neural network. We use all of the 1-min high-frequency transaction data of the CSI 300 futures contract (IF1704) in our empirical analysis, and we test three groups of different volume samples to validate our observations. We find that the deep learning method of predicting stock index futures outperforms the back propagation, the extreme learning machine, and the radial basis function neural network in its fitting degree and directional predictive accuracy. We also find that increasing the amount of data increases predictive performance. This indicates that deep learning captures the nonlinear features of transaction data and can serve as a powerful stock index futures prediction tool for financial market investors.