Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Experiences of Fibromyalgia Patients Involved in the Fimouv Study During COVID-19 Lockdown

Introduction: The COVID-19 pandemic implied a period of lockdown for the general population, increasing the risk to develop some physical or mental disorders. In fibromyalgia patients, these disorders are part of the large clinical picture of the syndrome. Fibromyalgia management is especially based on a regular practice of physical activity. Lockdown imposed a break in rhythms, requiring a restructuring of scheduling. Thus, the present study aimed to investigate the experiences of fibromyalgia patients during COVID-19 lockdown using a qualitative analysis. Method: 19 patients (52 ± 9 years old) who completed a 3-month therapeutic education and/or supervised physical activity program were invited to participate (Fimouv study, Trial registration: ClinicalTrials.gov NCT04107948). A sociologist collected data by means of semi-structured interviews and analyzed them using thematic content analysis. Results: Lockdown exacerbated the main symptoms of fibromyalgia, but adjusting the rhythms of life to fluctuations of these symptoms allowed a better quality of life. Patients felt the lack of physical activity and 68% found alternatives to remain physically active. The reduction of social constraints allowed them to better contend with their pathology. Fibromyalgia stopped being a main priority. Conclusion: Lockdown was positively experienced by fibromyalgia patients. They linked the absence of physical activity with increased pain and fatigue. Nevertheless, reducing social constraints could be a key for fibromyalgia management, where symptoms seemed to take less space in everyday life. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT04107948.
Electroacupuncture Regulates Pain Transition Through Inhibiting PKCε and TRPV1 Expression in Dorsal Root Ganglion

Many cases of acute pain can be resolved with few side effects. However, some cases of acute pain may persist beyond the time required for tissue injury recovery and transit to chronic pain, which is hard to treat. The mechanisms underlying pain transition are not entirely understood, and treatment strategies are lacking. In this study, the hyperalgesic priming model was established on rats to study pain transition by injection of carrageenan (Car) and prostaglandin E2 (PGE2). The expression levels of protein kinase C epsilon (PKCε) and transient receptor potential vanilloid 1 (TRPV1) in the L4–L6 dorsal root ganglion (DRG) were investigated. Electroacupuncture (EA) is a form of acupuncture in which a small electric current is passed between a pair of acupuncture needles. EA was administrated, and its effect on hyperalgesia and PKCε and TRPV1 expression was investigated. The PKCε–TRPV1 signaling pathway in DRG was implicated in the pain transition. EA increased the pain threshold of model animals and regulated the high expression of PKCε and TRPV1. Moreover, EA also regulated hyperalgesia and high TRPV1 expression induced by selective PKCε activation. We also found that EA partly increased chronic pain threshold, even though it was only administered between the Car and PGE2 injections. These findings suggested that EA could prevent the transition from acute to chronic pain by inhibiting the PKCε and TRPV1 expression in the peripheral nervous system.
A Validated Mathematical Model of the Cytokine Release Syndrome in Severe COVID-19

By June 2021, a new contagious disease, the Coronavirus disease 2019 (COVID-19), has infected more than 172 million people worldwide, causing more than 3.7 million deaths. Many aspects related to the interactions of the disease’s causative agent, SAR2-CoV-2, and the immune response are not well understood: the multiscale interactions among the various components of the human immune system and the pathogen are very complex. Mathematical and computational tools can help researchers to answer these open questions about the disease. In this work, we present a system of fifteen ordinary differential equations that models the immune response to SARS-CoV-2. The model is used to investigate the hypothesis that the SARS-CoV-2 infects immune cells and, for this reason, induces high-level productions of inflammatory cytokines. Simulation results support this hypothesis and further explain why survivors have lower levels of cytokines levels than non-survivors.
Diarylpentanoid (1,5-bis(4-hydroxy-3-methoxyphenyl)-1,4-pentadiene-3-one) (MS13) Exhibits Anti-proliferative, Apoptosis Induction and Anti-migration Properties on Androgen-independent Human Prostate Cancer by Targeting Cell Cycle–Apoptosis and PI3K Signalling Pathways

Diarylpentanoids exhibit a high degree of anti-cancer activity and stability in vitro over curcumin in prostate cancer cells. Hence, this study aims to investigate the effects of a diarylpentanoid, 1,5-bis(4-hydroxy-3-methoxyphenyl)-1,4-pentadiene-3-one (MS13) on cytotoxicity, anti-proliferative, apoptosis-inducing, anti-migration properties, and the underlying molecular mechanisms on treated androgen-independent prostate cancer cells, DU 145 and PC-3. A cell viability assay has shown greater cytotoxicity effects of MS13-treated DU 145 cells (EC50 7.57 ± 0.2 µM) and PC-3 cells (EC50 7.80 ± 0.7 µM) compared to curcumin (EC50: DU 145; 34.25 ± 2.7 µM and PC-3; 27.77 ± 6.4 µM). In addition, MS13 exhibited significant anti-proliferative activity against AIPC cells compared to curcumin in a dose- and time-dependent manner. Morphological observation, increased caspase-3 activity, and reduced Bcl-2 protein levels in these cells indicated that MS13 induces apoptosis in a time- and dose-dependent. Moreover, MS13 effectively inhibited the migration of DU 145 and PC-3 cells. Our results suggest that cell cycle-apoptosis and PI3K pathways were the topmost significant pathways impacted by MS13 activity. Our findings suggest that MS13 may demonstrate the anti-cancer activity by modulating DEGs associated with the cell cycle-apoptosis and PI3K pathways, thus inhibiting cell proliferation and cell migration as well as inducing apoptosis in AIPC cells.
Deconstruct, Analyse, Reconstruct: How to improve Tempo, Beat, and Downbeat Estimation

In this paper, we undertake a critical assessment of a state-of-the-art deep neural network approach for computational rhythm analysis. Our methodology is to deconstruct this approach, analyse its constituent parts, and then reconstruct it. To this end, we devise a novel multi-task approach for the simultaneous estimation of tempo, beat, and downbeat. In particular, we seek to embed more explicit musical knowledge into the design decisions in building the network. We additionally reflect this outlook when training the network, and include a simple data augmentation strategy to increase the network's exposure to a wider range of tempi, and hence beat and downbeat information. Via an in-depth comparative evaluation, we present state-of-the-art results over all three tasks, with performance increases of up to 6% points over existing systems.
Information Quality Assessment for Data Fusion Systems

This paper provides a comprehensive description of the current literature on data fusion, with an emphasis on Information Quality (IQ) and performance evaluation. This literature review highlights recent studies that reveal existing gaps, the need to find a synergy between data fusion and IQ, several research issues, and the challenges and pitfalls in this field. First, the main models, frameworks, architectures, algorithms, solutions, problems, and requirements are analyzed. Second, a general data fusion engineering process is presented to show how complex it is to design a framework for a specific application. Third, an IQ approach, as well as the different methodologies and frameworks used to assess IQ in information systems are addressed; in addition, data fusion systems are presented along with their related criteria. Furthermore, information on the context in data fusion systems and its IQ assessment are discussed. Subsequently, the issue of data fusion systems’ performance is reviewed. Finally, some key aspects and concluding remarks are outlined, and some future lines of work are gathered.
Chitosan Schiff base as eco-friendly inhibitor for mild steel corrosion in 1 M HCl

Abstract In the present study, natural biopolymer chitosan was modified into its Schiff base derivative with salicylaldehyde by condensation method. The prepared chitosan salicylaldehyde Schiff base was characterized using ultraviolet spectroscopy, fourier transform infrared spectroscopy, scanning electron microscope and elemental analysis. Thermal analysis was also carried out to determine the thermal stability of the derivative. To explore the corrosion inhibition performance of the chitosan Schiff base, weight loss, and electrochemical techniques were conducted. The inhibitor reduces the metallic corrosion by adsorbing on to the metal surface. The adsorption of chitosan Schiff base on mild steel surface in 1 M HCl follows Temkin isotherm model. Thermodynamic parameters of adsorption and corrosion process were calculated, which revealed the chemical nature of adsorption. SEM and energy dispersive X-ray spectroscopic analysis confirmed the formation of protective chitosan derivative layer on the mild steel surface.
Measuring disruption in song similarity networks

Investigating music with a focus on the similarity relations between songs, albums, and artists plays an important role when trying to understand trends in the history of music genres. In particular, representing these relations as a similarity network allows us to investigate the innovation presented by these entities in a multitude of points-of-view, including disruption. A disruptive object is one that creates a new stream of events, changing the traditional way of how a context usually works. The proper measurement of disruption remains as a task with large room for improvement, and these gaps are even more evident in the music domain, where the topic has not received much attention so far. This work builds on preliminary studies focused on the analysis of music disruption derived from metadata-based similarity networks, demonstrating that the raw audio can augment similarity information. We developed a case study based on a collection of a Brazilian local music tradition called Forro, that emphasizes the analytical and musicological potential of the musical disruption metric to describe and explain a genre trajectory over time.
Modeling perception with hierarchical prediction: Auditory segmentation with deep predictive coding locates candidate evoked potentials in EEG

The human response to music combines low-level expectations that are driven by the perceptual characteristics of audio with high-level expectations from the context and the listener’s expertise. This paper discusses surprisal based music representation learning with a hierarchical predictive neural network. In order to inspect the cognitive validity of the network’s predictions along their time-scales, we use the network’s prediction error to segment electroencephalograms (EEG) based on the audio signal. For this, we investigate the unsupervised segmentation of audio and EEG into events using the NMED-T dataset on passive nat-ural music listening. The conducted exploratory analysis of EEG at locations connected to peaks in prediction error in the network allowed to visualize auditory evoked potentials connected to local and global musical structures. This indicates the potential of unsupervised predictive learning with deep neural networks as means to retrieve musical structure from audio and as a basis to uncover the corresponding cognitive processes in the human brain.
The multiple voices of musical emotions: source separation for improving music emotion recognition models and their interpretability

Despite the manifold developments in music emotion recognition and related areas, estimating the emotional impact of music still poses many challenges. These are often associated to the complexity of the acoustic codes to emotion and the lack of large amounts of data with robust golden standards. In this paper, we propose a new computational model (EmoMucs) that considers the role of different musical voices in the prediction of the emotions induced by music. We combine source separation algorithms for breaking up music signals into independent song elements (vocals, bass, drums, other) and end-to-end state-of-the-art machine learning techniques for feature extraction and emotion modelling (valence and arousal regression). Through a series of computational experiments on a benchmark dataset using source-specialised models trained independently and different fusion strategies, we demonstrate that EmoMucs outperforms state-of-the-art approaches with the advantage of providing insights into the relative contribution of different musical elements to the emotions perceived by listeners.
Corrosion Inhibition and Adsorption Behavior of Punica granatum Extract on Mild Steel in Acidic Environments: Experimental and Theoretical Studies

The adsorption and corrosion inhibiting effect of aqueous extracts of Punica granatum (PNG) on mild steel in 1 M HCl and 0.5 M H2SO4 at 30 ± 1 °C was investigated using gravimetric, electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization techniques. The experimental findings revealed that PNG inhibited the corrosion reaction in both acid environments. Impedance results indicate that the extract organic matter was adsorbed on the metal/solution interface, while polarization data show that the extract behaved mostly as a mixed-type inhibitor. A theoretical study of the adsorption behavior of some of the components of the crude extracts was carried out in the framework of the density functional theory (DFT).
Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches

Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk. Besides that, deep learning methodologies that aimed for the same goal were also reviewed. In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms of both methodologies used and results obtained. Finally, considerations about the analyzed papers are conducted. The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in a prediction of a risk score. Although promising, future endeavors in this field should consider the possibility of the implementation of the methodology in clinical practice.
An Attention Mechanism Oriented Hybrid CNN-RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

The booming computational thinking and deep learning make it possible to construct agile, efficient, and robust deep learning-driven decision-making support engine for the operation of container terminal handling systems (CTHSs). Within the conceptual framework of computational logistics, an attention mechanism oriented hybrid convolutional neural network and recurrent neural network deep learning architecture (AMO-HCR-DLA) is proposed technically to predict the container terminal liner handling conditions that mainly include liner handling time (LHT) and total working time of quay crane farm (TWT-QCF) for a calling liner. Consequently, the container terminal oriented logistics generalized computation (CTO-LGC) automation and intelligence are established tentatively by AMO-HCR-DLA. A typical regional container terminal hub of China is selected to design, implement, execute, and evaluate the AMO-HCR-DLA with the actual production data. In the case of severe vibration of LHT and TWT-QCF, while forecasting the handling conditions of 210 ships based on the CTO-LGC running log of four years, the forecasting error of LHT within one hour is more than 97% and that of TWT-QCF within six hours accounts for 89.405%. When predicting the operating conditions of 300 liners by the log of five years, the forecasting deviation of LHT within one hour is more than striking 99% and that of TWT-QCF within six hours reaches up to 94.010% as well. All are far superior to the predicting outcomes by the classical algorithms of machine learning and deep learning. Hence, the AMO-HCR-DLA shows excellent performance for the prediction of CTHS with the low and stable computational consuming. It also demonstrates the feasibility, credibility, and realizability of the computing architecture and design paradigm of AMO-HCR-DLA preliminarily.
Comparison of polyphenols and anthocyanin content of different blackcurrant (Ribes nigrum L.) cultivars at the Polli Horticultural Research Centre in Estonia

The evaluation of blackcurrant cultivars and their fruit properties at the Polli Horticultural Research Centre has been active since 1945. In addition to the assessment of biological and economic properties of cultivars, it is essential to pay attention to fruit quality. In 2014, the laboratory building of Polli Horticultural Research Centre was reconstructed within the PlantValor competence centre project, enabling to introduce HPLC methods for the determination of polyphenolic compounds in fruit quality analysis. In 2017 and 2018, the fruit quality of 37 blackcurrant cultivars of different geographical origin (Belarus, Estonia, Finland, Latvia, Lithuania, Norway, Poland, Russia, Scotland, Sweden and Ukraine) was analysed. All cultivars were grown in the genetic resources collection (2008–2019) located at the Polli Horticultural Research Centre. The main aim of the study was to analyse the content of polyphenols and anthocyanins for selecting suitable blackcurrant genotypes for breeding programmes, fruit production and possible product development. In two consecutive years of the study, the total polyphenols content in the fruits of different cultivars varied 290–634 mg 100 g fresh weight (fw) and the anthocyanins 183–471 mg 100 g fw.
Sweet passion fruit yield and fruit quality related to fertilization with urea and cattle manure

ABSTRACT An experiment was conducted in Campos dos Goytacazes in the State of Rio de Janeiro, Brazil, in order to evaluate the applying effect of nitrogen and cattle manure doses on sweet passion fruit yield and quality. The experimental outline used was complete block, in a 5 × 2 factorial arrangement, five nitrogen doses (0, 50, 150, 250 and 350g plant−1 year−1) and two types of organic fertilization (with and without cattle manure), with 4 replications. Sweet passion fruit yield presented an increase of 44.4% due to the application of the best nitrogen doses in comparison with the treatment which did not receive nitrogen fertilization. The treatments that did not receive cattle manure presented higher average weight, diameter and thickness of the fruit skin, while the addition of cattle manure presented greater juice concentration and higher percentage of seeds in sweet passion fruit fruits. The increase in nitrogen doses allowed an increase in concentration and total titratable acidity of the fruits.
Combined Metabolic Activators Improves Cognitive Functions in Alzheimer's Disease

Alzheimer's disease (AD) is associated with metabolic abnormalities linked to critical elements of neurodegeneration. Here, we analyzed the brain transcriptomics data of more than 600 AD patients using genome-scale metabolic models and provided supporting evidence of mitochondrial dysfunction related to the pathophysiologic mechanisms of AD progression. Subsequently, we investigated, in an animal model of AD, the oral administration of Combined Metabolic Activators (CMAs), consisting of NAD+ and glutathione precursors, to explore the effect for improvement of biological functions in AD. CMAs includes L-serine, nicotinamide riboside, N-acetyl-L-cysteine, and L-carnitine tartrate, salt form of L-carnitine. The study revealed that supplementation of the CMAs improved the AD-associated histological parameters in the animals. Finally, we designed a randomized, double-blinded, placebo-controlled human phase 2 study and showed that the administration of CMAs improves cognitive functions in AD patients. A comprehensive analysis of the human plasma metabolome and proteome revealed that plasma levels of proteins and metabolites associated with redox metabolism are significantly improved after treatment. In conclusion, our results show that treating AD patients with metabolic activators leads to enhanced cognitive functions, suggesting a role for such a therapeutic regime in treating AD and other neurodegenerative diseases.
In vitro antioxidant activity test and determination of phenolic and flavonoid content of Moringa oleifera pulp and seeds

Moringa oleifera is a plant that is empirically widely used as a medicine to treat hyperglycemia, anti-inflammatory, antidiabetic, antimicrobial, anticancer, antioxidant, gastric ulcer, skin disease, fever, fatigue, hysteria, thrush, bladder, and bronchitis. Moringa oleifera contains secondary metabolites such as flavonoids, tannins, terpenoids, alkaloids, and phenolics. This study aimed to investigate the antioxidant activity and levels of phenolic and flavonoids in the extracts and fractions of moringa pulp and seeds. The pulp and seeds of Moringa were extracted using the maceration method and fractionated using the liquid-liquid extraction method. Antioxidant activity was measured using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method. Phenolic content was measured using the Folin-Ciocalteau method. Meanwhile, the flavonoid content was determined using the AlCl3 colorimetric method with UV-Vis spectrophotometry. The ethyl acetate fraction of moringa pulp and seeds had the strongest antioxidant activity with IC50 values of 5.97 µg/mL and 5.08 µg/mL, respectively. Ethyl acetate fraction also has high phenolic and flavonoid content in the pulp and seeds of moringa with a phenolic content of 43.956 mg GAE/g sample for pulp and 9.429 mg GAE/g sample for moringa seeds. The flavonoid content of 94.257 mg QE/g sample for pulp and 93.719 mg QE/g sample, with the highest correlation between phenolic compounds and flavonoids on antioxidant activity, was obtained in moringa pulp with the highest correlation (R2 = 0.6514) for phenolic and R 2 = 0.7531) for flavonoids. In conclusion, the ethyl acetate fraction of moringa pulp and seeds can be further developed as an antioxidant and functional food.
Super-resolution generative adversarial network (SRGAN) enabled on-chip contact microscopy

We demonstrate a deep learning based contact imaging on a CMOS chip to achieve ∼1 μm spatial resolution over a large field of view of ∼24 mm2. By using regular LED illumination, we acquire the single lower-resolution image of the objects placed approximate to the sensor with unit fringe magnification. For the raw contact-mode lens-free image, the pixel size of the sensor chip limits the spatial resolution. We apply a super-resolution generative adversarial networks, a type of deep learning based single-image super-resolution (SR) algorithm, to circumvent this limitation and effectively recover much higher resolution image of the objects, permitting sub-micron spatial resolution to be achieved across the entire sensor chip active area, which is also equivalent to the imaging field-of-view (24 mm2) due to unit magnification. This SR contact imaging approach eliminates the need of either lens or multi-frame acquisition, being very powerful and cost-effective. We demonstrate the success of this approach by imaging the proliferation dynamics of large-scale cells and the Instantaneous behaviors of freely moving Caenorhabditis elegans directly on the chip.