Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Energy Drink Consumption on Physical Performance and Potential Danger of Inordinate Usage

The rise in energy drink (ED) intake in the general population and athletes has been achieved with smart and effective marketing strategies. There is a robust base of evidence showing that adolescents are the main consumers of EDs. The prevalence of ED usage in this group ranges from 52% to 68%, whilst in adults is estimated at 32%. The compositions of EDs vary widely. Caffeine content can range from 75 to 240 mg, whereas the average taurine quantity is 342.28 mg/100 mL. Unfortunately, exact amounts of the other ED elements are often not disclosed by manufacturers. Caffeine and taurine in doses 3–6 mg/kg and 1–6 g, respectively, appear to be the main ergogenic elements. However, additive or synergic properties between them seem to be implausible. Because of non-unified protocol design, presented studies show inconsistency between ED ingestion and improved physical performance. Potential side effects caused by abusive consumption or missed contraindications are the aspects that are the most often overlooked by consumers and not fully elucidated by ED producers. In this review, the authors aimed to present the latest scientific information on ED components and their possible impact on improving physical performance as well as to bring emphasis to the danger of inordinate consumption.
Validation pré-clinique d'un produit d'ingénierie vasculaire à base d'hydrogel de chitosane

Objectifs. Les substituts vasculaires synthetiques sont a l’origine de nombreux echecs pour le remplacement des vaisseaux de petit calibre. L'emergence de l'ingenierie vasculaire ouvre des perspectives face a ce probleme de sante publique. Le chitosane, polymere naturel, peut etre utilise comme matrice en ingenierie vasculaire. L'objectif de cette these etait de produire des hydrogels de chitosane et d’etudier les proprietes mecaniques, biologiques et la biointegration. Materiel et Methodes. Les hydrogels ont ete caracterises mecaniquement in vitro. En augmentant la concentration en chitosane, la resistance a la suture, a l’eclatement et les modules elastiques augmentaient de maniere significative. Une serie d'experiences, allant de l’evaluation in vitro a l’analyse in vivo de la biocompatibilite a ete realisee. In vitro, les hydrogels permettaient la proliferation des progeniteurs endotheliaux (EPCs) et etaient hemocompatibles. In vivo, les hydrogels n’etaient pas resorbes apres 60 jours, dans un modele d'implantation heterotopique chez le rat. De plus, la biointegration du chitosane a ete etudiee. In vitro, l’endothelium a la surface des hydrogels se comportait comme celui d’un vaisseau natif. In vivo, les hydrogels de chitosane etaient capables de moduler la reponse inflammatoire, dans un modele d'implantation ectopique chez le rat, en favorisant la polarisation des macrophages vers le phenotype M2. Enfin, 2 tubes de chitosane ont ete implantes avec succes pour des pontages carotidiens pendant 3 jours chez le mouton. Conclusion. En modulant la concentration de chitosane, nous avons produit des matrices avec des proprietes adaptees pour l’ingenierie vasculaire.
The Treatment of Medication-Related Osteonecrosis of the Jaw (MRONJ): A Systematic Review with a Pooled Analysis of Only Surgery versus Combined Protocols

Medication-related osteonecrosis of the jaw (MRONJ) is a serious adverse reaction of antiresorptive and antiangiogenic agents, and it is also a potentially painful and debilitating condition. To date, no specific studies have prospectively evaluated the efficacy of its treatment and no robust standard of care has been established. Therefore, a systematic review (2007–2020) with a pooled analysis was performed in order to compare MRONJ surgical techniques (conservative or aggressive) versus combined surgical procedures (surgery plus a non-invasive procedure), where 1137 patients were included in the pooled analysis. A statistically significant difference in the 6-month improvement rate, comparing combined conservative surgery versus only aggressive (91% versus 72%, p = 0.05), was observed. No significant difference regarding any group with respect to the 6-month total resolution rate (82% versus 72%) was demonstrated. Of note, conservative surgery combined with various, adjuvant, non-invasive procedures (ozone, LLLT or blood component + Nd:YAG) was found to achieve partial or full healing in all stages, with improved results and the amelioration of many variables. In conclusion, specific adjuvant treatments associated with minimally conservative surgery can be considered effective and safe in the treatment of MRONJ, although well-controlled studies are a requisite in arriving at definitive statements
sFlt-1/PlGF Ratio in Prediction of Short-Term Neonatal Outcome of Small for Gestational Age Neonates

Background: Small for gestational age is a pregnancy complication associated with a variety of adverse perinatal outcomes. The aim of the study was to investigate if sFlt-1/PlGF ratio is related to adverse short-term neonatal outcome in neonates small for gestational age in normotensive pregnancy. Methods: A prospective observational study was conducted. Serum sFlt-1/PlGF ratio was measured in women in singleton gestation diagnosed with fetus small for gestational age. Short-term neonatal outcome analyzed in the period between birth and discharge home. Results: Eighty-two women were included. Women with sFlt-1/PlGF ratio ≥33 gave birth to neonates with lower birthweight at lower gestational age. Neonates from high ratio group suffered from respiratory disorders and NEC significantly more often. They were hospitalized at NICU more often and were discharged home significantly later. sFlt-1/PlGF ratio predicted combined neonatal outcome with sensitivity of 73% and specificity of 82.2%. Conclusions: sFlt-1/PlGF ratio is a useful toll in prediction of short-term adverse neonatal outcome in SGA pregnancies.
Changing the Basis of Contextual Representations with Explicit Semantics

The application of transformer-based contextual representations has became a de facto solution for solving complex NLP tasks. Despite their successes, such representations are arguably opaque as their latent dimensions are not directly interpretable. To alleviate this limitation of contextual representations, we devise such an algorithm where the output representation expresses human-interpretable information of each dimension. We achieve this by constructing a transformation matrix based on the semantic content of the embedding space and predefined semantic categories using Hellinger distance. We evaluate our inferred representations on supersense prediction task. Our experiments reveal that the interpretable nature of transformed contextual representations makes it possible to accurately predict the supersense category of a word by simply looking for its transformed coordinate with the largest coefficient. We quantify the effects of our proposed transformation when applied over traditional dense contextual embeddings. We additionally investigate and report consistent improvements for the integration of sparse contextual word representations into our proposed algorithm.
Folded Bio-Polymers – Structure, Pulling and Diffusion

In this work denaturation transitions of nucleic acids and bio-polymers in and out of equilibrium are studied theoretically. First, the over-stretching transition of DNA is modeled successfully by a three-state model, which allows to disentangle effects of the stacking energy of base pairs and the loop free energy. Second, the thermodynamics of RNA folding is studied: A marked dependence on the parameterization of loops, which are a common theme in folded RNA structures, and on salt concentration is found. Including these effects yields perfect agreement with experimental data. Third, coarse grained Brownian dynamics simulations of homopolymers reveal the influence of cohesive strength and chain length on internal friction. The existence of two distinct globular phases is shown.
Health Issues Due to the Global Prevalence of Sedentariness and Recommendations towards Achieving a Healthier Behaviour

Sedentariness has progressed in recent years. Here, we summarize the high prevalence of objectively measured sedentariness and the list of health problems associated with sedentariness. According to the literature, a minimum sedentary time of 8 h/d may avoid the harmful effects of sedentariness. Our review of the literature shows that many countries worldwide exceed this threshold. The coronavirus disease 2019 pandemic has increased the proportion of time spent seated in chairs and/or other types of furniture. Furthermore, prolonged sedentariness will continue to increase because it is assumed that people, at least those in desk jobs, perform their work better when sitting than when standing. Many practical solutions should be implemented to help people reduce their sedentary time. People need to be aware that prolonged sedentariness causes health problems. They need to measure the amount of time spent being sedentary to self-guide their behaviour. They should adopt a new lifestyle to avoid prolonged sedentariness and prolonged standing. In addition, we point out that they should frequently change their posture to avoid fatigue and health issues. For global public health, there is an urgent need to adopt an intermediate healthy/healthier behaviour between too much time spent in the sitting and standing positions.
Prevalence of SARS-CoV-2 Antibodies after First 6 Months of COVID-19 Pandemic, Portugal

In September 2020, we tested 13,398 persons in Portugal for antibodies against severe acute respiratory syndrome coronavirus 2 by using a quota sample stratified by age and population density. We found a seroprevalence of 2.2%, 3–4 times larger than the official number of cases at the end of the first wave of the pandemic.
Horizontal Gene Transfers in Plants

In plants, as in all eukaryotes, the vertical transmission of genetic information through reproduction ensures the maintenance of the integrity of species. However, many reports over the past few years have clearly shown that horizontal gene transfers, referred to as HGTs (the interspecific transmission of genetic information across reproductive barriers) are very common in nature and concern all living organisms including plants. The advent of next-generation sequencing technologies (NGS) has opened new perspectives for the study of HGTs through comparative genomic approaches. In this review, we provide an up-to-date view of our current knowledge of HGTs in plants.
Heterogeneous Wafer Bonding Technology and Thin-Film Transfer Technology-Enabling Platform for the Next Generation Applications beyond 5G

Wafer bonding technology is one of the most effective methods for high-quality thin-film transfer onto different substrates combined with ion implantation processes, laser irradiation, and the removal of the sacrificial layers. In this review, we systematically summarize and introduce applications of the thin films obtained by wafer bonding technology in the fields of electronics, optical devices, on-chip integrated mid-infrared sensors, and wearable sensors. The fabrication of silicon-on-insulator (SOI) wafers based on the Smart CutTM process, heterogeneous integrations of wide-bandgap semiconductors, infrared materials, and electro-optical crystals via wafer bonding technology for thin-film transfer are orderly presented. Furthermore, device design and fabrication progress based on the platforms mentioned above is highlighted in this work. They demonstrate that the transferred films can satisfy high-performance power electronics, molecular sensors, and high-speed modulators for the next generation applications beyond 5G. Moreover, flexible composite structures prepared by the wafer bonding and de-bonding methods towards wearable electronics are reported. Finally, the outlooks and conclusions about the further development of heterogeneous structures that need to be achieved by the wafer bonding technology are discussed.
Role of Phytonutrients in Nutrigenetics and Nutrigenomics Perspective in Curing Breast Cancer

Breast cancer (BC) is one of the most common type of cancer and an important contributor to female mortality. Several genes and epigenetic modifications are involved in the development and progression of BC. Research in phytochemistry, nutrigenomics, and nutrigenetics has provided strong evidence that certain phytonutrients are able to modulate gene expression at transcriptional and post-transcriptional levels. Such phytonutrients may also be beneficial to prevent and treat BC. In this review, we will focus on the nutrigenomic effects of various phytochemicals including polyphenols, phytosterols, terpenoids, alkaloids, and other compounds from different sources. Overall, these phytonutrients are found to inhibit BC cell proliferation, differentiation, invasion, metastasis, angiogenesis, and induce apoptotic cell death by targeting various molecular pathways. They also alter epigenetic mechanisms and enhance the chemosensitivity and radiosensitivity of cancer cells. Such phytochemicals may be used for the effective management of BC patients in the clinical setting in the future. The present article aims to summarize the specific molecular pathways involved in the genetic effects of phytochemicals in BC.
Reliable Fabrication of Graphene Nanostructure Based on e-Beam Irradiation of PMMA/Copper Composite Structure

Graphene nanostructures are widely perceived as a promising material for fundamental components; their high-performance electronic properties offer the potential for the construction of graphene nanoelectronics. Numerous researchers have paid attention to the fabrication of graphene nanostructures, based on both top-down and bottom-up approaches. However, there are still some unavoidable challenges, such as smooth edges, uniform films without folds, and accurate dimension and location control. In this work, a direct writing method was reported for the in-situ preparation of a high-resolution graphene nanostructure of controllable size (the minimum feature size is about 15 nm), which combines the advantages of e-beam lithography and copper-catalyzed growth. By using the Fourier infrared absorption test, we found that the hydrogen and oxygen elements were disappearing due to knock-on displacement and the radiolysis effect. The graphene crystal is also formed via diffusion and the local heating effect between the e-beam and copper substrate, based on the Raman spectra test. This simple process for the in-situ synthesis of graphene nanostructures has many promising potential applications, including offering a way to make nanoelectrodes, NEMS cantilever resonant structures, nanophotonic devices and so on.
Starch Solutions Prepared under Different Conditions as Modifiers of Chitosan/Poly(aspartic acid)-Based Hydrogels

Recently, there has been great interest in the application of polysaccharides in the preparation of diverse biomaterials which result from their biocompatibility, biodegradability and biological activity. In this work, the investigations on chitosan/poly(aspartic acid)-based hydrogels modified with starch were described. Firstly, a series of hydrogel matrices was prepared and investigated to characterize their swelling properties, structure via FT-IR spectroscopy, elasticity and tensile strength using the Brookfield texture analyzer as well as their impact on simulated physiological liquids. Hydrogels consisting of chitosan and poly(aspartic acid) in a 2:1 volume ratio were elastic (9% elongation), did not degrade after 30-day incubation in simulated physiological liquids, exhibited a relative biocompatibility towards these liquids and similar swelling in each absorbed medium. This hydrogel matrix was modified with starch wherein two of its form were applied—a solution obtained at an elevated temperature and a suspension obtained at room temperature. Hydrogels modified with hot starch solution showed higher sorption that unmodified materials. This was probably due to the higher starch inclusion (i.e., a larger number of hydrophilic groups able to interact with the adsorbed liquid) when this polysaccharide was given in the form of a hot solution. Hydrogels modified with a cold starch suspension had visible heterogeneous inequalities on their surfaces and this modification led to the obtainment materials with unrepeatable structures which made the analysis of their properties difficult and may have led to misleading conclusions.
Soluble Guanylate Cyclase Stimulator Vericiguat Enhances Long-Term Memory in Rats without Altering Cerebral Blood Volume

Vascular cognitive impairment (VCI) is characterized by impairments in cerebral blood flow (CBF), endothelial function and blood–brain barrier (BBB) integrity. These processes are all physiologically regulated by the nitric oxide (NO)-soluble guanylate cyclase (sGC)-cGMP signaling pathway. Additionally, cGMP signaling plays an important role in long-term potentiation (LTP) underlying memory formation. Therefore, targeting the NO-sGC-cGMP pathway may be a therapeutic strategy for treating VCI. Hence, in this study we investigated whether sGC stimulator vericiguat has potential as a cognitive enhancer. The effects of vericiguat on long-term memory were measured in rats using an object location task. Due to the low brain-penetrance of vericiguat found in this study, it was investigated whether in the absence of BBB limitations, vericiguat enhanced hippocampal plasticity using an ex vivo memory acquisition-like chemical LTP model. Finally, peripheral effects were measured by means of blood pressure and cerebral blood volume. Vericiguat successfully enhanced long-term memory and increased hippocampal plasticity via enhanced translocation of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptors to the cell membrane, while blood pressure and cerebral blood volume were unaltered. Although the memory enhancing effects in this study are likely due to peripheral effects on the cerebral microvasculature, sGC stimulation may provide a new therapeutic strategy for treating VCI, especially when BBB integrity is reduced.
COVID-19 and Misinformation: A Large-Scale Lexical Analysis on Twitter

Social media is often used by individuals and organisations as a platform to spread misinformation. With the recent coronavirus pandemic we have seen a surge of misinformation on Twitter, posing a danger to public health. In this paper, we compile a large COVID-19 Twitter misinformation corpus and perform an analysis to discover patterns with respect to vocabulary usage. Among others, our analysis reveals that the variety of topics and vocabulary usage are considerably more limited and negative in tweets related to misinformation than in randomly extracted tweets. In addition to our qualitative analysis, our experimental results show that a simple linear model based only on lexical features is effective in identifying misinformation-related tweets (with accuracy over 80%), providing evidence to the fact that the vocabulary used in misinformation largely differs from generic tweets.
GCN-RNN: An Unified Framework for Modeling the multi-label diagnosis of chest X-ray disease

Chest X-ray is one of the most common radiology exams used for screening of lung diseases. A typical chest X-ray image might contain multiple thoracic diseases, which is a multi-label learning problem. Multi-label learning is a supervised classification framework in which multiple target labels can be assigned to one instance. How to effectively capture the correlations between object labels and explore these label correlations to improve the classification performance are both important for multi-label image recognition. In this paper, we presented a combination of GCN and RNN(LSTM) to exploit label dependencies from global and local perspective. Experimental results on public CheXpert dataset demonstrate that the proposed architecture achieves better performance than the state-of-the-art classification models.
Multiple RTS and DATA Receptions in a Loop-Based Underwater Propagation Delay Aware MAC Protocol

Due to the long underwater propagation delays, the underwater sink node may not receive all of the requests to send RTS and DATA frames from the various underwater sensor nodes, resulting in massive collisions. We proposed an underwater Medium Access Control (MAC) protocol that takes into account the long propagation delay to evaluate the performance of the carrier sense multiple access collision avoidance (CSMA/CA) protocol utilizing a loop-based single hope underwater sensor network. The proposed protocol is designed to restore multiple RTS control packets from n underwater sensor nodes to the underwater sink node in a single slot time. The sink node received multiple RTS frames without overlapping because the n underwater sensor nodes transmitted the RTS frames at the same time, but they were received at different times at the receiver end due to differences in propagation delays that depend on the distances between the n sensor nodes and the sink node. In this paper, by altering the number of underwater sensor nodes and the size of the contention window, the proposed MAC protocol throughput is evaluated by using the underwater propagation delay.
Coarse-to-fine Kidney Segmentation Incorporating Abnormality Detection and Correction

In this paper, we propose and validate a coarse-to-fine kidney segmentation method from Computed Tomography (CT) images, i.e., predicting a coarse label based on the entire image and a fine label based on the coarse segmentation and cropped image patches. A key difference between the two stages lies in how input images were preprocessed. For the coarse segmentation, each 2D CT slice was normalized to be of the same image size (but possible different pixel size), and for the fine segmentation, each 2D CT slice was first resampled to be of the same pixel size and then cropped to be of the same image size. In other words, the image inputs to the coarse segmentation were 2D CT slices of the same image size whereas those to the fine segmentation were 2D CT patches of the same image size as well as the same pixel size. In addition, we designed an abnormality detection method based on component analysis between two stages and used another 2D convolutional neural network to correct the abnormality regions. A total of 168 CT images were used to train the proposed framework and evaluations were conducted qualitatively on another 42 testing images. The proposed method showed promising results and achieved an average DSC of 94.53 % on the testing data.