Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-emotion extraction from text based on linguistic analysis

Emotions as one of the important elements of human nature are a part of everyday communications of people. We can distinguish person's emotions from some outcome behaviors such as speech, facial expression, body movements and gestures. Another outcome behavior that reflects the inner states of the person is his/her grammar and written method. Because nowadays, people are more likely to use textual tools to make connection, emotion extraction from the text has attracted a lot of attention. This paper provides a framework for the extraction of emotions in the text. By considering that a text may contain more than one emotion that only one of them is text dominant emotion, our proposed method, has modeled emotion extraction problem as a multi-label classification problem by removing the fixed boundaries of emotions, and recognizes all the existing emotions in the sentence and also dominant emotion. The proposed method extracts emotions by using structural and semantic information in the sentence, linguistic information and machine learning techniques. The experiments have been done on multi-label dataset contains 629 sentences with eight emotional categories. Based on the results, our proposed method, compared with used multi-label learning methods (BR, RAKEL, MLANN) have shown a better performance.
Selective Blockade of T-Type Ca2+ Channels is Protective Against Alcohol-Withdrawal Induced Seizure and Mortality

Aims We have previously demonstrated that blockade of T-type calcium channels by the non-selective antagonist, ethosuximide (ETX), is effective at reducing electrographical and behavioral correlates of alcohol-withdrawal (WD) seizure. Here, we investigated whether blockade of these calcium channels with the selective antagonist TTA-P2 also reduces alcohol-WD seizure. Short summary The non-specific T-type calcium channel antagonist, ETX, is protective against alcohol-WD seizure. However, the mechanism of this effect is unclear. Here, we provide evidence that further suggests selective blockade of T-type calcium channels are protective against alcohol-WD seizure and WD-related mortality. Methods We used an intermittent ethanol exposure model to produce WD-induced hyperexcitability in DBA/2 J mice. Seizure severity was intensified with the chemoconvulsant pentylenetetrazole (PTZ). Results TTA-P2 (10 mg/kg) reduced seizure severity in mice undergoing alcohol WD with concurrent PTZ treatment (20 mg/kg). Moreover, TTA-P2 (20 and 40 mg/kg) was also protective against PTZ-induced (40 mg/kg) seizure and mortality. Conclusions These results are consistent with prior results using ETX, and suggest that the protective effects of ETX and TTA-P2 against EtOH WD seizures are mediated by T-type calcium channels.
Effectiveness of Interventions to Modulate the Rumen Microbiota Composition and Function in Pre-ruminant and Ruminant Lambs

Modulating the assembly of the ruminal microbiota might have practical implications in production. We tested how an early-life dietary intervention in lambs influences the diversity and function of the ruminal microbiota during and after the intervention. Microbiota resilience during a repeated dietary intervention was also tested. The treatment, aiming to mitigate enteric methane emissions, combined garlic essential oil and linseed oil. Fifty-six lambs and their dams were allocated to two groups and treatment (T1) or placebo (C1) was drenched from birth until 10 weeks of life. Lambs were weaned at 8 weeks. From 16 to 20 weeks, lambs in each group were divided in two subgroups that received (T1–T2 and C1–T2) or not (T1–C2 and C1–C2) the same treatment. Measurements were done at 8, 14, and 20 weeks. Average daily gain was similar between groups. Methane production was reduced by treatment at 8 and 20 weeks but at 14 weeks it was similar between C1 and T1. Interestingly, early-life treated lambs displayed a numerical increase (P = 0.12) in methane emissions at 20 weeks compared with non-treated lambs. Concentration of VFA was not affected by the intervention at 8 or 14 weeks but a lower concentration was observed in T2 lambs compared with C2 at week 20. Metataxonomics (rRNA gene) revealed differences in archaeal communities between groups of lambs when treatment was applied (weeks 8 and 20); whereas, in accord with methane emissions, these differences disappeared when treatment was discontinued (week 14). Protozoal community structure was not affected by treatment. In contrast, bacterial community structure differed between treated and non-treated lambs during and after the intervention. Rumen and urine LC-MS and NMR metabolomics at week 20 separated C2 from T2 lambs and correlation analysis highlighted interactions between microbes and metabolites, notably that of methylated compounds and Methanomassiliicocceae methanogens. This study demonstrates that a long-term early-life intervention induced modifications in the composition of the rumen bacterial community that persisted after the intervention ceased with little or no effect on archaeal and protozoal communities. However, there was no persistency of the early-life intervention on methanogenesis indicating resilience for this function.
Plasmids of Psychrotolerant Polaromonas spp. Isolated From Arctic and Antarctic Glaciers – Diversity and Role in Adaptation to Polar Environments

Cold-active bacteria of the genus Polaromonas (class Betaproteobacteria) are important components of glacial microbiomes. In this study, extrachromosomal replicons of 26 psychrotolerant Polaromonas strains, isolated from Arctic and Antarctic glaciers, were identified, sequenced, and characterized. The plasmidome of these strains consists of 13 replicons, ranging in size from 3,378 to 101,077 bp. In silico sequence analyses identified the conserved backbones of these plasmids, composed of genes required for plasmid replication, stable maintenance, and conjugal transfer. Host range analysis revealed that all of the identified plasmids are narrow-host-range replicons, only able to replicate in bacteria of closely related genera (Polaromonas and Variovorax) of the Comamonadaceae family. Special attention was paid to the identification of plasmid auxiliary genetic information, which may contribute to the adaptation of bacteria to environmental conditions occurring in glaciers. Detailed analysis revealed the presence of genes encoding proteins potentially involved in (i) protection against reactive oxygen species, ultraviolet radiation, and low temperatures; (ii) transport and metabolism of organic compounds; (iii) transport of metal ions; and (iv) resistance to heavy metals. Some of the plasmids also carry genes required for the molecular assembly of iron–sulfur [Fe-S] clusters. Functional analysis of the predicted heavy metal resistance determinants demonstrated that their activity varies, depending on the host strain. This study provides the first molecular insight into the mobile DNA of Polaromonas spp. inhabiting polar glaciers. It has generated valuable data on the structure and properties of a pool of plasmids and highlighted their role in the biology of psychrotolerant Polaromonas strains and their adaptation to the environmental conditions of Arctic and Antarctic glaciers.
Using Nectar-Related Traits to Enhance Crop-Pollinator Interactions

Floral nectar and other reward facilitate crop pollination, and in so doing, increase the amount and breadth of food available for humans. Though abundance and diversity of pollinators (particularly bees) have declined over the past several decades, a concomitant increase in reliance on pollinators presents a challenge to food production. Development of crop varieties with specific nectar or nectar-related traits to attract and retain pollinating insects is an appealing strategy to help address needs of agriculture and pollinators for several reasons. First, many crops have specific traits which have been identified to enhance crop–pollinator interactions. Also, an improved understanding of mechanisms that govern nectar-related traits suggest simplified phenotyping and breeding are possible. Finally, the use of nectar-related traits to enhance crop pollination should complement other measures promoting pollinators and will not limit options for crop production or require any changes by growers (other than planting varieties that are more attractive or rewarding to pollinators). In this article, we review the rationale for improving crop-pollinator interactions, the effects of specific plant traits on pollinator species, and use cultivated sunflowers as a case study. Recent research in sunflower has (i) associated variation in bee visitation with specific floral traits, (ii) quantified benefits of pollinators to hybrid yields, and (iii) used genetic resources in sunflower and other plants to find markers associated with key floral traits. Forthcoming work to increase pollinator rewards should enable sunflower to act as a model for using nectar-related traits to enhance crop–pollinator interactions.
Cumulative Participant Selection with Switch Costs in Large-Scale Mobile Crowd Sensing

With the rapid increasing of the number of mobile devices and their embedded sensing technologies, mobile crowd sensing (MCS) has become an emerging modern sensing paradigm for performing large-scale urban sensing. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of appropriate participants from the huge user pool to perform the sensing tasks. The capability of a particular user for certain task depends on many factors, such as her moving pattern/behavior, device capability, sensor quality, or even uploading bandwidth. Many of these information of participants are unknown by the selection mechanism. Therefore, self-learning based approaches have been proposed to learn the users' capability for certain tasks via multiple trials and their online performances. In this paper, we first model the cumulative participant selection problem as a combinational multi-armed bandit problem and present an online selection algorithm which leverages the historical performing records of participants to learn the different capabilities (both sensing probability and time delay) of participants. Further, to consider the cost of switching participant for particular tasks, we then introduce the cumulative participant selection problem with switch costs and propose a corresponding online learning method. For both proposed learning algorithms, we provide regret analysis. In addition, extensive simulations with real- world mobile datasets are conducted for the evaluations of the proposed methods. Our simulation results confirm the effeteness of them.
Ensemble learning methods for power system cyber-attack detection

Power system is one of the most important industrial control systems in today's society. In recent years, power systems have been well researched and developed extensively with a high rate. In order to optimally integrate systems and reduce costs, lots of advanced information technologies are involved into power systems. Traditional power system is changing to the smart power grid rapidly. Therefore, modern power systems are now exposing to the public network and information security is becoming a new threat to resilience. In this work, we explore the suitability of ensemble learning methods as a means of detecting power system cyber-attack. We evaluate various ensemble learning methods as cyber-attack detectors and discuss the practical implications for deploying ensemble learning methods as an enhancement to existing power system architectures.
Match-Play Running Performance and Exercise Intensity in Elite International Women's Rugby Sevens.

Malone, S, Earls, M, Shovlin, A, Eddy, A, and Winkleman, N. Match-play running performance and exercise intensity in elite international women's rugby sevens. J Strength Cond Res XX(X): 000-000, 2018-The aim of the current investigation was to describe the running and physiological performance demands of elite women's rugby sevens match-play. Twenty-seven (n = 27) rugby seven's players (24.4 ± 2.1 years; 168 ± 7.1 cm; 67.9 ± 4.3 kg) were recruited for the current investigation. Across the observational period, 36 games were analyzed; during these games, players wore global positioning system technology (10-Hz, Statsports Viper Pod; STATSports, Newry, Northern Ireland, United Kingdom) and heart rate monitors (Polar Team System, Polar Electro Oy, Kempele, Finland). A total of 250 individual player data sets were obtained for final analysis. Players were categorized based on positional groups; backs and forwards, and monitored across halves of play. The mean distance covered during match-play was 1,625 ± 132 m which equates to a relative running performance of 116.8 ± 9.4 m·min. The high-speed distance of players was 199 ± 44 m, which equates to a relative high-speed running performance of 14.2 ± 3.1 m·min. Significant reductions in high-speed running (p = 0.003; effect size [ES]: 0.23; 90% confidence interval [CI]: 0.11-0.41) and significant increases in lower speed running were observed across halves of play (p = 0.04; ES: 0.33; 90% CI: 0.16-0.54). Across the duration of match-play, players spent over 75% of the time above 80% of heart rate maximum (HRmax). Backs were found to have a higher reduction in total distance (p = 0.345; ES: 0.21; 90% CI: 0.11-0.31), high-speed distance (p = 0.04; ES: 0.61; 90% CI: 0.48-0.77), sprint distance (p = 0.034; ES: 0.11; 90% CI: 0.02-0.21), and average sprint distance (p = 0.03; ES: 0.33; 90% CI: 0.08-0.44) across halves of play when compared to forwards. Normative data are now provided to coaches who need to consider the positional differences in running and physiological performance when constructing training drills for seven's players.
Analog Haptic Control: Advantages and Challenges

Haptic simulation systems, which typically implement virtual environments in the discrete-time domain, present an inherent trade-off between stability, sampling frequency, and the range of implementable environment dynamics. Previous research has demonstrated the potential of analog feedback for expanding the range of environment dynamics that result in a stable haptic interaction. In this paper, the effect of various system parameters on the environment dynamic range is analytically and experimentally investigated in the sense of uncoupled stability. In addition, Multilayer Decomposition, which enables a simple analog PD controller to implement nonhomogeneous or multilayer virtual environment dynamics, will be presented and evaluated.
Three-Year Survival Comparison between Transcatheter and Surgical Aortic Valve Replacement for Intermediate- and Low-Risk Patients

Transcatheter aortic valve implantation is a suitable therapeutic intervention for patients deemed inoperable or high risk for surgical aortic valve replacement. Current investigations question whether it is a suitable alternative to surgery for intermediate- and low-risk patients. The following meta-analysis presents a comparison between transcatheter versus surgical aortic valve replacement in patients that are intermediate and low risk for surgery. Articles were collected via an electronic search using Google Scholar and PubMed. Articles of interest included studies comparing the survival of intermediate- and low-risk patients undergoing transcatheter aortic valve implantation to those undergoing surgical aortic valve replacement. Primary end points included 1-, 2-, and 3-year survival. Secondary end points included postintervention thromboembolic events, stroke, transient ischemic attacks, major vascular complications, permanent pacemaker implantation, life-threatening bleeding, acute kidney injury, atrial fibrillation, and moderate-to-severe aortic regurgitation. Six studies met the criteria for the meta-analysis. One- and two-year survival comparisons showed no difference between the two interventions. Surgical aortic valve replacement, however, presented with favorable 3-year survival compared with the transcatheter approach. Transcatheter aortic valve implantation had more major vascular complications, permanent pacemaker implantation, and moderate-to-severe aortic regurgitation rates compared with surgery. Surgical aortic valve replacement presented more life-threatening bleeding, acute kidney injury, and atrial fibrillation compared with a transcatheter approach. There was no statistical difference between the two approaches in terms of thromboembolic events, strokes, or transient ischemic attack rates. Surgical aortic valve replacement presents favorable 3-year survival rates compared with transcatheter aortic valve implantation.
HearthBot: An Autonomous Agent Based on Fuzzy ART Adaptive Neural Networks for the Digital Collectible Card Game HearthStone

Digital collectible card games, as partially observable games based on alternating turns, such as HearthStone, have been the most played card games in recent years, where the main challenge is the creation of strategies capable of subdue the enemy's moves. From the artificial intelligence perspective, the space of possible strategies is large and dynamic due to the number of cards and actions combinations and also to randomness, which makes the design of efficient autonomous agents a hard problem. This paper presents HearthBot, an autonomous agent that plays HearthStone through an adaptive neural network inspired in the fuzzy adaptive resonance associative map and adaptive resonance theory map. This paper also proposes a new mechanism to categorize and predict information to overcome the overgeneralization problem from those networks. Furthermore, the proposed solution was implemented as a parallel adaptive neural network for a graphics processing unit that achieves a performance compatible with the ones obtained for deep learning methods. HearthBot win rate was evaluated in two experiments playing against a Monte Carlo tree search heuristic with competitive decks on a HearthStone simulator called Metastone. Results show that the proposed solution allows HearthBot to obtain an average win rate performance of 80% against known decks and 70% against unknown decks.
Focal and diffuse cervical spinal cord damage in patients with early relapsing–remitting MS: A multicentre magnetisation transfer ratio study

Background: Studies including patients with well-established multiple sclerosis (MS) have shown a significant and disability-related reduction in the cervical spinal cord (SC) magnetisation transfer ratio (MTR). Objectives: The objectives are to (1) assess whether MTR reduction is already measurable in the SC of patients with early relapsing–remitting multiple sclerosis (RRMS) and (2) describe its spatial distribution. Methods: We included 60 patients with RRMS <12 months and 34 age-matched controls at five centres. Axial T2*w, sagittal T2w, sagittal phase-sensitive inversion recovery (PSIR), 3DT1w, and axial magnetisation transfer (MT) images were acquired from C1 to C7. Lesions were manually labelled and mean MTR values computed both for the whole SC and for normal-appearing SC in different regions of interest. Results: Mean whole SC MTR was significantly lower in patients than controls (33.7 vs 34.9 pu, p = 0.00005), even after excluding lesions (33.9 pu, p = 0.0003). We observed a greater mean reduction in MTR for vertebral levels displaying the highest lesion loads (C2–C4). In the axial plane, we observed a greater mean MTR reduction at the SC periphery and barycentre. Conclusion: Cervical SC tissue damage measured using MTR is not restricted to macroscopic lesions in patients with early RRMS and is not homogeneously distributed.
Learning multiagent coordination in the absence of communication channels

In this work, we develop a reinforcement learning protocol for a multiagent coordination task in a discrete state and action space: an iterated prisoner's dilemma game extended into a team based, winner-take all tournament, which forces the agents to collude in order to maximize their reward. By disallowing extra communication channels, the agents are forced to embed their coordination strategy into their actions in the prisoner's dilemma game. We develop a representation of the iterated prisoners dilemma that makes it amenable to Q-learning. We find that the reinforcement learning strategy is able to consistently train agents that can win the winner take all iterated prisoners dilemma tournament. By using a game with discrete state and action space, we are able to better analyze and understand both the dynamics and the communication protocols that are established between the agents. We find that the agents adapt a number of interesting behaviors, such as the formation of benevolent dictators, that minimize inequality of scores. We also find that the agents settle on a remarkably consistent symbology in their actions, such that agents from independent trials are able to collude with each other without further training.
Foreword 3 Wound Assessment 4 Wound Biofilms 2012 7 Quality of Life 10 Case Study 12 Closing the Link 13

The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or not-for-profit purposes without prior permission or charge, provided:
An Approach for Repairing Process Models Based on Logic Petri Nets

Process mining technology can extract process knowledge from event logs that are generated from information systems. It can construct a process model by mining the event logs. The model usually needs to be repaired to accurately describe the business process realized by the information system. The existing methods for repairing process models cannot enhance efficiently some model consistency metrics, such as the fitness, precision, and simplicity. Thus, a new model repair approach is proposed based on an extended Petri net named logic Petri net in this paper. It can improve the model’s fitness and precision comparing with the existing work. First, it builds process models via logic Petri nets. Next, approaches are proposed to repair the process models containing a causal relation and a concurrent relation, respectively. Specifically, a precursor set and a successor set of activities are defined and the relation of the elements in each of them is determined. Finally, we give some cases related to a thoracic surgery process in a hospital and conduct experiments to illustrate the correctness and effectiveness of the proposed approach.