Miracle-dz/UniFAR-FTUs huggingface dataset:
Adding vibration to high‐intensity intervals increase time at high oxygen uptake in well‐trained cyclists

The importance of accumulated time ≥90% of maximal oxygen consumption (VO2max) to improve performance in well‐trained endurance athletes is well established. The present study compared the acute effects of adding vibrations (VIB; 40 Hz) with the work intervals during a high‐intensity cycling session (HIT) with a traditional HIT session without vibration (TRAD) on time ≥90% of VO2max, time ≥90% of peak heart rate (HRpeak), electromyography (EMG) activity, and mean power in well‐trained cyclists (n = 10, VO2max=78.6 ± 7.4 mL/min/kg). The order of VIB and TRAD was randomized and consisted of 6 × 5‐minutes work intervals performed with the highest possible mean power across the work intervals (2.5‐minutes standardized relief periods). VIB was superior to TRAD on time ≥90% of VO2max, (10.99 ± 7.00 vs 6.95 ± 5.28 minutes, respectively), time ≥90% of HRpeak (24.61 ± 2.38 vs 19.97 ± 4.12 minutes, respectively), and averaged EMG activity in m. Vastus Lateralis during the work intervals (all P < 0.05). The EMG/power output ratio across all work intervals was higher in VIB than TRAD (P < 0.05). Mean values across work intervals showed no difference between VIB and TRAD in mean power, rate of perceived exertion, or blood lactate concentration. Thus, the present study indicated that adding vibration to the work intervals during a HIT session can acutely increase the physiological responses of the cardiovascular system and increase time ≥90% VO2max and should therefore be considered in order to optimize the exercise stimulus of well‐trained cyclists.
Real-time Scholarly Retweeting Prediction System

Twitter has become one of the most import channels to spread latest scholarly information because of its fast information spread speed. How to predict whether a scholarly tweet will be retweeted is a key task in understanding the message propagation within large user communities. Hence, we present the real-time scholarly retweeting prediction system that retrieves scholarly tweets which will be retweeted. First, we filter scholarly tweets from tracking a tweet stream. Then, we extract Tweet Scholar Blocks indicating metadata of papers. At last, we combine scholarly features with the Tweet Scholar Blocks to predict whether a scholarly tweet will be retweeted. Our system outperforms chosen baseline systems. Additionally, our system has the potential to predict scientific impact in real-time.
Evaluation of positional accuracy in multiple implants using four different splinting materials: An in vitro study

Background: Prosthesis misfit plays an important role in complications such as occlusal and abutment screw loosening and fracture in implant restorations. Reproducing the intraoral relationship of implants through impression procedures is the first step in achieving an accurate, passively fitting prosthesis to ensure maximum accuracy. Hence, splinting of multiple implants with most accurate material may be a valid option. Context: The results of splinting multiple implants are always inconsistent, and there is limited literature available to compare the accuracy of impression techniques as well as materials. In such situation, more and more studies have to be done to prove the efficacy and accuracy of each splinting materials. Aim: The aim of the study was to evaluate the positional accuracy in multiple implants using acrylic resin, pattern resin, flowable composite, and bite registration paste. Subjects and Methods: An acrylic resin model was fabricated into which four implant analogs placed. The copings attached were splinted with dental floss onto which acrylic resin was added, which was sectioned and rewelded (Group A). Similarly, pattern resin, flowable composite, and bite registration material were added which were considered as Group B, C, and D, respectively. Impressions were made in vinyl polysiloxane and analogs were attached. The casts retrieved from each group were analysed for the positional accuracy of implants. Statistical Analysis: One-way ANOVA was done to analyze the significant difference between the four implant analogs of each group with the master model. The confidence interval was also calculated to assess the accuracy. Results: It was observed that all materials are equally effective for the accurate reproduction of implant positions. There was no significant difference between the centroids of implant analogues of master model and the mean of interimplant distance 1 and 2, 1 and 4, 3 and 4 and 2 and 4 in each group. The P-values were >0.05. The accuracy of splinting materials were analysed and it showed that splinting with flowable composite (Group C) as well as bite registration paste were in par with the conventionally used materials like pattern resin and acrylic resin. Conclusions: Results showed that flowable composite as well as bite registration material can be recommended as splinting material of choice for multiple implant cases, as these exhibited similar results like other groups (pattern resin and acrylic resin) which are conventionally used.
How does the robot feel? Perception of valence and arousal in emotional body language

Abstract Human-robot interaction in social robotics applications could be greatly enhanced by robotic behaviors that incorporate emotional body language. Using as our starting point a set of pre-designed, emotion conveying animations that have been created by professional animators for the Pepper robot, we seek to explore how humans perceive their affect content, and to increase their usability by annotating them with reliable labels of valence and arousal, in a continuous interval space. We conducted an experiment with 20 participants who were presented with the animations and rated them in the two-dimensional affect space. An inter-rater reliability analysis was applied to support the aggregation of the ratings for deriving the final labels. The set of emotional body language animations with the labels of valence and arousal is available and can potentially be useful to other researchers as a ground truth for behavioral experiments on robotic expression of emotion, or for the automatic selection of robotic emotional behaviors with respect to valence and arousal. To further utilize the data we collected, we analyzed it with an exploratory approach and we present some interesting trends with regard to the human perception of Pepper’s emotional body language, that might be worth further investigation.
Diatoms do radiate: evidence for a freshwater species flock

Due to the ubiquity and high dispersal capacity of unicellular eukaryotes, their often extraordinary diversity found in isolated and long‐lived ecosystems such as ancient lakes is typically attributed to multiple colonization events rather than to in situ speciation. However, respective evolutionary studies are very scarce and the often high number of species flocks in ancient lakes across multicellular taxa raises the question whether unicellular species, such as diatoms, may radiate as well. Here, we use an integrative approach that includes molecular data from benthic diatom species of the genus Aneumastus endemic to ancient Lake Ohrid, fossil data obtained from the sediment record of a recent deep‐drilling project and biogeographical information to test if this group, indeed, constitutes a species flock. Molecular‐clock and phylogenetic analyses indicate a young monophyletic group of several endemic species. Molecular, fossil and biogeographical data strongly suggest a rapid intralacustrine diversification, which was possibly triggered by the emergence of novel habitats. This finding is the first evidence for a species flock in diatoms and suggests that in situ speciation is also a relevant evolutionary process for unicellular eukaryotes in isolated ecosystems.
Sentiment analysis system in big data environment

Nowadays, Big Data, a large volume of both structured and unstructured data, is generated from Social Media. Social Media are powerful marketing tools and social big data can offer the business insights. The major challenge facing social big data is attaining efficient techniques to collect a large volume of social data and extract insights from the huge amount of collected data. Sentiment Analysis of social big data can provide business insights by extracting the public opinions. The traditional analytic platforms need to be scaled up for analyzing a large volume of social big data. Social data are by nature shorter and generally not constructed with proper grammatical rules and hence difficult to achieve high reliable result in Sentiment Analysis. Acquiring effective training data is a challenge, although learning based approaches are good for sentiment classification. Manual Labeling for training data is time and labor consuming. In this paper, Sentiment Analysis system on Big Data Analytics platform is proposed to provide valuable information by analyzing large scale social data in an efficient and timely manner since they have been implemented using a MapReduce framework and a Hadoop distributed storage (HDFS). The proposed Sentiment Analysis system consists of four modules: data collection, data cleaning and preprocessing, class labeling and sentiment classification. The system enables high-level performance of sentiment classification while taking advantage of combining lexicon-based classifier’s effortless setup process and learning based classifier. Twitter stream data is used for system evaluation as the Twitter is widespread Social Media and a good source of information in the sense of snapshots of moods and feelings as well as up-to-date events. The evaluation results show that this system achieve a promising accuracy by 84.2%. Moreover, this system is able to scale up to analyze the large scale data by decreasing the processing time when adding more nodes in the cluster
Neural Relation Classification with Text Descriptions

Relation classification is an important task in natural language processing fields. State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given. However, these methods usually suffer from the data sparsity issue greatly. On the other hand, we notice that it is very easily to obtain some concise text descriptions for almost all of the entities in a relation classification task. The text descriptions can provide helpful supplementary information for relation classification. But they are ignored by most of existing methods. In this paper, we propose DesRC, a new neural relation classification method which integrates entities’ text descriptions into deep neural networks models. We design a two-level attention mechanism to select the most useful information from the “intra-sentence” aspect and the “cross-sentence” aspect. Besides, the adversarial training method is also used to further improve the classification per-formance. Finally, we evaluate the proposed method on the SemEval 2010 dataset. Extensive experiments show that our method achieves much better experimental results than other state-of-the-art relation classification methods.
Learning to Generate Word Representations using Subword Information

Distributed representations of words play a major role in the field of natural language processing by encoding semantic and syntactic information of words. However, most existing works on learning word representations typically regard words as individual atomic units and thus are blind to subword information in words. This further gives rise to a difficulty in representing out-of-vocabulary (OOV) words. In this paper, we present a character-based word representation approach to deal with this limitation. The proposed model learns to generate word representations from characters. In our model, we employ a convolutional neural network and a highway network over characters to extract salient features effectively. Unlike previous models that learn word representations from a large corpus, we take a set of pre-trained word embeddings and generalize it to word entries, including OOV words. To demonstrate the efficacy of the proposed model, we perform both an intrinsic and an extrinsic task which are word similarity and language modeling, respectively. Experimental results show clearly that the proposed model significantly outperforms strong baseline models that regard words or their subwords as atomic units. For example, we achieve as much as 18.5% improvement on average in perplexity for morphologically rich languages compared to strong baselines in the language modeling task.
Overcoming drug-resistant lung cancer by paclitaxel-loaded hyaluronic acid-coated liposomes targeted to mitochondria

Abstract As a major cause for the inefficiency of cancer chemotherapy, multidrug resistance (MDR) has become a major barrier to cancer treatment. Mitochondrion-orientated transportation of smart liposomes has been developed as a promising strategy to deliver anticancer drugs directly to tumor sites and actively target the mitochondria, so that drugs can interfere with mitochondrial function and facilitate cell apoptosis, overcoming MDR. Herein, we report a novel dual-functional paclitaxel (PTX) liposome system possessing both CD44-targeting and mitochondrial-targeting properties to enhance drug accumulation in mitochondria and trigger apoptosis of drug-resistant cancer cells. Mitochondria-targeting PTX-loaded liposomes were prepared by thin-film hydration and then coated with hyaluronic acid (HA) by electrostatic adsorption. We evaluated the characteristics of the PTX liposomes in vitro, and found that their particle size was about 100 nm and increased to ∼140 nm after modification by HA. The entrapment efficiency was larger than 85%, and stability data indicated that the liposomes were physically and chemically stable for at least one week at 4 °C. We further evaluated the intake, mitochondrial targeting, ATP levels, caspase-3 activity measurement, and antitumor actives of the liposomes. The results indicated that HA-coated liposomes with mitochondria targeting had significant inhibitory effects against A549 and A549/Taxol cells, showing them to be a promising means of improving therapeutic efficacy and overcoming MDR in cancer treatment.
Exploiting Structure in Representation of Named Entities using Active Learning

Fundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions. However, entities lack a unique representation and their mentions can differ greatly. These variations arise in complex ways that cannot be captured using textual similarity metrics. However, entities have underlying structures, typically shared by entities of the same entity type, that can help reason over their name variations. Discovering, learning and manipulating these structures typically requires high manual effort in the form of large amounts of labeled training data and handwritten transformation programs. In this work, we propose an active-learning based framework that drastically reduces the labeled data required to learn the structures of entities. We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels. Our experiments show that our framework consistently outperforms both handwritten programs and supervised learning models. We also demonstrate the utility of our framework in relation extraction and entity resolution tasks.
Molecular biological tools in concrete biodeterioration – a mini review

ABSTRACT Concrete structures develop biofilms when exposed to various environments. At a certain stage, the microbial films destroy the concrete structures leading to significant deterioration. Culture-dependent techniques give an incomplete picture of the microbial communities on the concrete surface. Culture-independent techniques or molecular biological tools pave a new way to analyse microbial communities involved in concrete biodeterioration. This study highlights the need to ‘build’ a database, for Microbiologically Influenced Concrete Corrosion (MICC) involving microbial groups that are being identified using culture-dependent and independent techniques. The role of molecular tools such as 16S rRNA sequencing, denaturing gradient gel electrophoresis (DGGE), Fluorescent in situ hybridization (FISH), Real-time Polymerase Chain Reaction (RT–PCR), microarray analysis, 2-Dimensional gel electrophoresis (2-DE) in analysing microbial communities on the concrete structures have been reviewed in this paper. GRAPHICAL ABSTRACT
Toward Accurate Quantitative Elasticity Mapping of Rigid Nanomaterials by Atomic Force Microscopy: Effect of Acquisition Frequency, Loading Force, and Tip Geometry

Atomic force microscopy (AFM) has emerged as a popular tool for the mechanical mapping of soft nanomaterials due to its high spatial and force resolution. Its applications in rigid nanomaterials, however, have been underexplored. In this work, we studied elasticity mapping of common rigid materials by AFM, with a focus on factors that affect the accuracy of elasticity measurements. We demonstrated the advantages in speed and noise level by using high frequency mechanical mapping compared to the classical force volume mapping. We studied loading force dependency, and observed a consistent pattern on all materials, where measured elasticity increased with loading force before stabilizing. Tip radius was found to have a major impact on the accuracy of measured elasticity. The blunt tip with 200 nm radius measured elasticity with deviation from nominal values up to 13% in different materials, in contrast to 122% by the sharp tip with 40 nm radius. Plastic deformation is believed to be the major reason for this difference. Sharp tips, however, still hold advantages in resolution and imaging capability for nanomaterials.
Enhanced Oxygen Vacancies in a Two-Dimensional MnAl-Layered Double Oxide Prepared via Flash Nanoprecipitation Offers High Selective Catalytic Reduction of NOx with NH3

A two-dimensional MnAl-layered double oxide (LDO) was obtained by flash nanoprecipitation method (FNP) and used for the selective catalytic reduction of NOx with NH3. The MnAl-LDO (FNP) catalyst formed a particle size of 114.9 nm. Further characterization exhibited rich oxygen vacancies and strong redox property to promote the catalytic activity at low temperature. The MnAl-LDO (FNP) catalyst performed excellent NO conversion above 80% at the temperature range of 100–400 °C, and N2 selectivity above 90% below 200 °C, with a gas hourly space velocity (GHSV) of 60,000 h−1, and a NO concentration of 500 ppm. The maximum NO conversion is 100% at 200 °C; when the temperature in 150–250 °C, the NO conversion can also reach 95%. The remarkable low-temperature catalytic performance of the MnAl-LDO (FNP) catalyst presented potential applications for controlling NO emissions on the account of the presentation of oxygen vacancies.
Design, Kinematics and Controlling a Novel Soft Robot Arm with Parallel Motion

This article presents a novel design for a double bend pneumatic muscle actuator (DB-PMA) inspired by snake lateral undulation. The presented actuator has the ability to bend in opposite directions from its two halves. This behavior results in horizontal and vertical movements of the actuator distal ends. The kinematics for the proposed actuator are illustrated and experiments conducted to validate its unique features. Furthermore, a continuum robot arm with the ability to move in parallel (horizontal displacement) is designed with a single DB-PMA and a two-finger soft gripper. The performance of the soft robot arm presented is explained, then another design of the horizontal motion continuum robot arm is proposed, using two self-bending contraction actuators (SBCA) in series to overcome the payload effects on the upper half of the soft arm.
Improving the accessibility of biomedical texts by semantic enrichment and definition expansion

We present work aimed at facilitating the comprehensibility of healthrelated English-Spanish parallel texts by means of the semantic annotation of biomedical concepts and the automatic expansion of their definitions. In order to overcome the limitations posed by the scarcity of resources available for Spanish, we propose to exploit existing tools targeted at English and then transfer the produced annotations. The evaluations performed show the feasibility of this approach. An enriched set of texts is made available, which can be retrieved, visualized and downloaded through a web interface.
Altered anabolic signalling and reduced stimulation of myofibrillar protein synthesis after feeding and resistance exercise in people with obesity

Lifestyle modifications that include the regular performance of exercise are probably important for counteracting the negative consequences of obesity on postprandial myofibrillar protein synthetic responses to protein dense food ingestion. We show that the interactive effect of resistance exercise and feeding on the stimulation of myofibrillar protein synthesis rates is diminished with obesity compared to normal weight adults. The blunted myofibrillar protein synthetic response with resistance exercise in people with obesity may be underpinned by alterations in muscle anabolic signalling phosphorylation (p70S6K and 4E‐BP1). The results obtained in the present study suggest that further exercise prescription manipulation may be necessary to optimize post‐exercise myofibrillar protein synthesis rates in adults with obesity.
Structured Representation Learning for Online Debate Stance Prediction

Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users’ conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.
Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content

With the increasing popularity of smart devices, rumors with multimedia content become more and more common on social networks. The multimedia information usually makes rumors look more convincing. Therefore, finding an automatic approach to verify rumors with multimedia content is a pressing task. Previous rumor verification research only utilizes multimedia as input features. We propose not to use the multimedia content but to find external information in other news platforms pivoting on it. We introduce a new features set, cross-lingual cross-platform features that leverage the semantic similarity between the rumors and the external information. When implemented, machine learning methods utilizing such features achieved the state-of-the-art rumor verification results.