Miracle-dz/UniFAR-FTUs huggingface dataset:
In Situ Electrochemical AFM Imaging of a Pt Electrode in Sulfuric Acid under Potential Cycling Conditions

Understanding the electrochemical behavior of Pt at the solid/liquid interface is of significant importance for the development of efficient electrochemical devices, such as fuel cells and water electrolyzers. In this work, the evolution of the surface morphology of a polycrystalline platinum under potential cycling conditions was investigated by in situ electrochemical atomic force microscopy (EC-AFM). After 50 cycles between 0.05 and 1.8 V in 0.1 M H2SO4, the Pt surface is coarsened and nanoparticles of several nanometers appear on the surface. The critical upper and lower potentials for the formation of nanoparticles are found to be 1.8 and 0.8 V, respectively. The in situ AFM observation coupled with Cyclic Voltammerty reveals the periodic disappearance and reappearance of the nanoparticles, based on which the formation of nanoparticles is attributed to the deposition of dissolved Pt from solution, and a model for the nanoparticle formation is proposed. While the formation of a thick oxide layer is a prerequisite, the reduction process is found to have a strong influence on Pt nanoparticle formation as well. This investigation provides a visualization of the Pt electrode surface under electrochemical control in a large potential window, enabling a broader understanding of the Pt electrode roughening mechanisms.
GPR17 mediates ischemia-like neuronal injury via microglial activation

GPR17 is a G (i)-coupled dual receptor, linked to P2Y and CysLT receptors stimulated by uracil nucleotides and cysteinyl leukotrienes, respectively. Recent evidence has demonstrated that GPR17 inhibition ameliorates the progression of cerebral ischemic injury by regulating neuronal death and microglial activation. The present study aimed to assess the detailed regulatory roles of this receptor in oxygen-glucose deprivation/recovery (OGD/R)-induced ischemia-like injury in vitro and explore the underlying mechanism. The results demonstrated that OGD/R induced ischemic neuronal injury and microglial activation, including enhanced phagocytosis and increased inflammatory cytokine release in neuron‑glial mixed cultures of cortical cells. GPR17 upregulation during OGD/R was spatially and temporally correlated with neuronal injury and microglial activation. In addition, GPR17 knockdown inhibited OGD/R-induced responses in neuron-glial mixed cultures. GPR17 knockdown also attenuated cell injury induced by the agonist leukotriene D4 (LTD4) or uridine 5′-diphosphate (UDP) in neuron-glial mixed cultures. However, GPR17 knockdown did not affect OGD/R-induced ischemic neuronal injury in primary cultures of neurons. In primary astrocyte cultures, neither GPR17 nor OGD/R induced injury. By contrast, GPR17 knockdown ameliorated OGD/R-induced microglial activation, boosting phagocytosis and inflammatory cytokine release in primary microglia cultures. Finally, the results demonstrated that the conditioned medium of microglia pretreated with OGD/R induced neuronal death, and the neuronal injury was significantly inhibited by GPR17 knockdown. These findings suggested that GPR17 may mediate ischemia-like neuronal injury and microglial activation in vitro; however, the protective effects on ischemic neuronal injury might depend upon microglial activation. Whether GPR17 regulates neuronal injury mediated by oligodendrocyte linkage remains to be investigated.
Transmission Dynamics of Highly Pathogenic Avian Influenza Virus A(H5Nx) Clade 2.3.4.4, North America, 2014–2015

Eurasia highly pathogenic avian influenza virus (HPAIV) H5 clade 2.3. 4.4 emerged in North America at the end of 2014 and caused outbreaks affecting >50 million poultry in the United States before eradication in June 2015. We investigated the underlying ecologic and epidemiologic processes associated with this viral spread by performing a comparative genomic study using 268 full-length genome sequences and data from outbreak investigations. Reassortant HPAIV H5N2 circulated in wild birds along the Pacific flyway before several spillover events transmitting the virus to poultry farms. Our analysis suggests that >3 separate introductions of HPAIV H5N2 into Midwest states occurred during March–June 2015; transmission to Midwest poultry farms from Pacific wild birds occurred ≈1.7–2.4 months before detection. Once established in poultry, the virus rapidly spread between turkey and chicken farms in neighboring states. Enhanced biosecurity is required to prevent the introduction and dissemination of HPAIV across the poultry industry.
Association between Cigarette Smoking Status and Composition of Gut Microbiota: Population-Based Cross-Sectional Study

There have been few large-scale studies on the relationship between smoking and gut microbiota. We investigated the relationship between smoking status and the composition of gut microbiota. This was a population-based cross-sectional study using Healthcare Screening Center cohort data. A total of 758 men were selected and divided into three groups: never (n = 288), former (n = 267), and current smokers (n = 203). Among the three groups, there was no difference in alpha diversity, however, Jaccard-based beta diversity showed significant difference (p = 0.015). Pairwise permutational multivariate analysis of variance (PERMANOVA) tests between never and former smokers did not show a difference; however, there was significant difference between never and current smokers (p = 0.017) and between former and current smokers (p = 0.011). Weighted UniFrac-based beta diversity also showed significant difference among the three groups (p = 0.038), and pairwise PERMANOVA analysis of never and current smokers showed significant difference (p = 0.01). In the analysis of bacterial composition, current smokers had an increased proportion of the phylum Bacteroidetes with decreased Firmicutes and Proteobacteria compared with never smokers, whereas there were no differences between former and never smokers. In conclusion, gut microbiota composition of current smokers was significantly different from that of never smokers. Additionally, there was no difference in gut microbiota composition between never and former smokers.
A Major Facilitator Superfamily Transporter Regulated by the Stress-Responsive Transcription Factor Yap1 Is Required for Resistance to Fungicides, Xenobiotics, and Oxidants and Full Virulence in Alternaria alternata

Alternaria alternata relies on the ability to produce a host-selective toxin and to detoxify reactive oxygen species to successfully colonize the host. An A. alternata major facilitator superfamily transporter designated AaMFS54 was functionally characterized by analysis of loss- and gain-of-function mutations to better understand the factors required for fungal pathogenesis. AaMFS54 was originally identified from a wild-type expression library after being subtracted with that of a mutant impaired for the oxidative stress-responsive transcription regulator Yap1. AaMFS54 contains 14 transmembrane helixes. Fungal mutant lacking AaMFS54 produced fewer conidia and increased sensitivity to many potent oxidants (potassium superoxide and singlet-oxygen generating compounds), xenobiotics (2,3,5-triiodobenzoic acid and 2-chloro-5-hydroxypyridine), and fungicides (clotrimazole, fludioxonil, vinclozolin, and iprodione). AaMFS54 mutant induced necrotic lesion sizes similar to those induced by wild-type on leaves of susceptible citrus cultivars after point inoculation with spore suspensions. However, the mutant produced smaller colonies and less fluffy hyphae on the affected leaves. Virulence assays on citrus leaves inoculated by spraying with spores revealed that AaMFS54 mutant induced less severe lesions than wild-type, indicating the requirement of AaMFS54 in pathogenesis. All defective phenotypes were restored in a strain re-acquiring a functional copy of AaMFS54. Northern blotting analysis revealed that the expression of AaMFS54 was suppressed by xenobiotics. The current studies indicate that the Yap1-mediated transporter plays a role in resistance to toxic oxidants and fungicides in A. alternata. The relationships of MFS transporters with other regulatory components conferring stress resistance and A. alternata pathogenesis are also discussed.
Recognizing Minimal Facial Sketch by Generating Photorealistic Faces With the Guidance of Descriptive Attributes

Cross-modal sketch-photo recognition is of vital importance in law enforcement and public security. Most existing methods are dedicated to bridging the gap between the low-level visual features of sketches and photo images, which is limited due to intrinsic differences in pixel values. In this paper, based on the intuition that sketches and photo images are highly correlated in the semantic domain, we propose to jointly utilize the low-level visual features and high-level facial attributes to enhance the representation ability of sketches. More specifically, a Multi-Modal Conditional GAN (MMC-GAN) is proposed to generate face images for further face recognition based on the generated images. During training, an identity-preserving constraint is further introduced to improve the discriminative ability of the synthetic images. Extensive experiments demonstrate that the effectiveness of attribute-aided face synthesis and recognition.
Negation Handling using Stacking Ensemble Method

Polarity shift is the major problem in the Bag-of-words model. Polarity shifting occurs when the polarity of the sentence is different from the polarity expressed by the sum of the content words in the sentence. Polarity shift reverses the sentiment polarity of the text. It affects the classification performance of the machine learning algorithms. Negation, contrast and sentiment inconsistency are the three different types of polarity shifts. The proposed system consists of four modules namely preprocessing, polarity shift detection and elimination, sentiment classification, and stacking ensemble method. Polarity shift detection is a hybrid approach. It is a combination of rule and statistic based method. Linear Support Vector Machine, Logistic Regression and Naive Bayes are used for sentiment classification. The Weka tool is used for the implementation of the machine learning algorithms. Stacking ensemble method combines the output of the base classifiers and gives the integrated model. The paper focuses on the document level sentiment analysis. Stacking ensemble method helps to increase the accuracy of the machine learning algorithms. The system is analyzed using airline reviews. Airline reviews are taken from Skytrax website. Reviews are categorized into four types namely airline, airport, lounge, and seat. Best airline of the year can be identified by the proposed system.
Instlistener: An Expressive Parameter Estimation System Imitating Human Performances of Monophonic Musical Instruments

We present InstListener, a system that takes an expressive monophonic solo instrument performance by a human performer as the input and imitates its audio recordings by using an existing MIDI (Musical Instrument Digital Interface) synthesizer. It automatically analyzes the input and estimates, for each musical note, expressive performance parameters such as the timing, duration, discrete semitone-level pitch, amplitude, continuous pitch contour, and continuous amplitude contour. The system uses an iterative process to estimate and update those parameters by analyzing both the input and output of the system so that the output from the MIDI synthesizer can be similar enough to the input. Our evaluation results showed that the iterative parameter estimation improved the accuracy of imitating of the input performance and thus increased the naturalness and expressiveness of the output performance.
Balance Strategy for Human Imitation by a NAO Humanoid Robot

This paper presents an ankle-based balance strategy for a NAO humanoid robot while imitating the human motions. In this approach, first, an inverted pendulum model based on the computed Center of Mass (CoM) is introduced and then, the support polygon is computed for each double support and single support phases. Center of the support polygon is assumed as the reference for balance controller and Ground projection of the Center of Mass (GCoM) is considered as the balance criteria. Using ankle joints correction, GCoM is restricted to the center of the support polygon. In order to control the balance criteria a Proportional-Integral-Derivative (PID) controller is used. The coefficients are first estimated using Ziegler-Nichols method; then, they were tuned by considering advantages of the imitation process. Implementation of the proposed approach leads to a better result in preserving the balance of the robot in soft realtime imitation of human whole-body and quasi-static motions. The proposed approach is validated by performing simulation and practical tests on a NAO H-25 version 4 robot.
Antioxidant Activity and In Vitro Antiglycation of the Fruit of Spondias purpurea

Hyperglycemia in diabetes mellitus causes irreversible life-threatening micro- and macrovascular complications. There is evidence that the glycation reaction leads to a chemical modification of the proteins contributing to the complications of diabetes. It is known that advanced glycation end products (AGEs) are formed by glycation and oxidation reactions called glycoxidation. CML, a nonfluorescent AGE, has become a biomarker of glycoxidative damage; other AGEs appear to induce oxidative stress, which results in cytotoxicity. To determine antioxidant activity, the FRAP, DPPH, and TEAC tests were used, as well as the polyphenols content using Folin-Ciocalteu's method. To evaluate the antiglycation activity, the BSA/glucose system was used, and the fructosamine concentration, protein carbonyl content, thiol, and CML groups were determined. The results obtained show that the hexane extract of the fruit of Spondias purpurea (CFH) effectively inhibits the glycation reaction, in addition to increasing the thiol groups and decreasing levels of fructosamine, protein carbonyl, and CML. In addition, CFH presented significant antioxidant activity. CFH inhibits the glycation reaction; therefore, it can help prevent complications related to AGEs in diabetes mellitus; it also reduces oxidative stress and is effective in protecting proteins from oxidative damage.
Modeling Gaming QoE: Towards the Impact of Frame Rate and Bit Rate on Cloud Gaming

Recent advances of streaming services and the upcoming new generation of mobile networks, 5G, offering low end-to-end delay as well as high bandwidths, promise a bright future for cloud gaming applications. Cloud gaming, in the contrary to traditional gaming services, suffers not only from system factors on the client, but is also affected significantly by the network, server specification and encoding parameters. In this paper, we present the results of a subjective experiment aiming to investigate the impact of two influencing factors, frame rate and bit rate, on the gaming Quality of Experience. The results reveal that a trade-off between an acceptable video quality and interaction quality exists. In case of very low bit rates, lowering the frame rate can improve the video quality while at some point, jerkiness becomes visible which affects the video quality negatively and the control over the game will be strongly reduced. Furthermore, even though in the gaming community a frame rate of 60 fps is desired, no significant difference for quality ratings, as well as performance ratings, was found between 60 fps and 25 fps. Therefore, it would be highly valuable for service providers to find an ideal strategy for this issue. In addition, we investigate the impact of video encoding on gaming experience dimensions. Finally, a first attempt to model the impact of two influencing factors on overall quality will be presented.
Sentiment Analysis of Tweets Using Semantic Analysis

In today's world, there is endless stream of data present online and the automated analysis of such data holds a great promise in business analytics for providing a strong support in decision making. This paper looks at the very heart of the concept of sentiment analysis by classifying the tweets with the help of algorithms like Naïve Bayes, Maximum Entropy, and Negation. In this paper, we first preprocess the tweets to remove unnecessary content in tweet; we then extract the adjectives which forms the feature vector, which are also used to find synonyms used in further semantic calculations in aforementioned algorithms. Finally, we calculate Accuracy, Precision, and Recall to compare these algorithms.
Towards Scalable Information-Seeking Multi-Domain Dialogue

Multi -domain dialogue systems face challenges such as scaling algorithms to handle large ontologies, or transferring trained policy models to unseen domains. We attempt to address these challenges by proposing a dialogue management architecture that has an abstracted view of the world but yet is able to focus on relevant parts of the ontology at runtime. Specifically, we train a sub-domain identifier neural network that learns which features are relevant to the current turn and the immediate future, thus filtering out irrelevant information from the ontology and consequently the belief space at each dialogue turn. We then train a policy network that needs: a) to adapt to the sub-domain identifier's output; and b) to learn what information will carry over from previous turns and when it needs to be updated. We evaluate our method on a large information-seeking ontology that contains latent sub-domains. Our results in simulation and a small human trial show that the sub-domain identifier is able to generalise to unseen domains and achieve performance on par with a multi-domain dialogue manager where each sub-domain is carefully defined (golden standard).
Grasping Flat Objects by Exploiting Non-Convexity of the Object and Support Surface

In this paper we propose a grasp strategy which exploits environmental contact for grasping domestic flat objects placed or hinged on support surfaces. The proposed grasp strategy considers the non-convex geometry of the object-surface combination, as this appears in objects like plates on tables or handles on cupboards. Following the fact that state-of-the-art grasp planners fail to produce candidate grasps for flat objects due to the environmental constraint of the support surface, this work utilizes compliant interaction of the hand with the support surface, inspired by human grasp strategies.
First Autonomous Multi-Room Exploration with an Insect-Inspired Flapping Wing Vehicle

One of the emerging tasks for Micro Air Vehicles (MAVs) is autonomous indoor navigation. While commonly employed platforms for such tasks are micro-quadrotors, insect-inspired flapping wing MAVs can offer many advantages, such as being inherently safe due to their low inertia, reciprocating wings bouncing of objects or potentially lower noise levels compared to rotary wings. Here, we present the first flapping wing MAV to perform an autonomous multi-room exploration task. Equipped with an on-board autopilot and a 4 g stereo vision system, the DelFly Explorer succeeded in combining the two most common tasks of an autonomous indoor exploration mission: room exploration and door passage. During the room exploration, the vehicle uses stereo-vision based droplet algorithm to avoid and navigate along the walls and obstacles. Simultaneously, it is running a newly developed monocular color based Snake-gate algorithm to locate doors. A successful detection triggers the heading-based door passage algorithm. In the real-world test, the vehicle could successfully navigate, multiple times in a row, between two rooms separated by a corridor, demonstrating the potential of flapping wing vehicles for autonomous exploration tasks.
Energy Measurement of Encryption Techniques Using RAPL

Now days, uses of handheld devices increases day-by-day which leads to handle the security issues accurately. Encryption techniques plays important role in security services. Lightweight encryption techniques are more suitable for handheld devices. This requires the adoption of energy efficient encryption techniques. Different energy measurement approaches are available at software level as well as hardware level to estimate the energy efficiency. In this paper, we have compared the popular cryptographic techniques such as Casear cipher, RSA, T-DES and AES at various levels. Energy consumption for these various cryptographic algorithms is measured at CPU, DRAM and package level using Running Average Power Limit (RAPL). RAPL allow to measure energy cost more effectively at less expenses.
Embryonic development of teleost Brycon orbignyanus

Summary Brycon orbignyanus is an important large teleost that is currently on the list of endangered species, therefore studies on its reproductive biology and embryology are fundamental to help species conservation and recovery. The objective of this research was to characterize the events that occur during extrusion, fertilization and embryonic development of the species. The samples were collected at predetermined times, fixed and processed for light microscopy and scanning electron microscopy. The greenish oocytes were spherical, had translucent chorion and a mean diameter of 1.3±0.11 mm. The eggs had well defined animal and vegetative poles approximately 18 min post-fertilization. Stages from 2 to 128 blastomeres occurred between 20 min and 3 h post-fertilization (hPF), when the morula was characterized. The blastula stage was observed between 2 and 3 hPF, and the gastrula between 3 and 7 hPF, when the embryonic shield emerged and the cellular migration with the consequent formation of epiblast and hypoblast. At 8 hPF, the formation of the neural tube, above the notochord and the encephalic region, was observed, delimiting the forebrain, mesencephalon and rhombencephalon regions. From 11 hPF onward, the optic vesicle was formed close to the forebrain and the embryo tail was well developed. The optic vesicle was observed from 12 hPF onward, and the tail showed an intense movement that culminated with the rupture of the chorion and consequent hatching of the larva at 13 hPF and 27°C.
Spatiotemporal Attention Based Deep Neural Networks for Emotion Recognition

We propose a spatiotemporal attention based deep neural networks for dimensional emotion recognition in facial videos. To learn the spatiotemporal attention that selectively focuses on emotional sailient parts within facial videos, we formulate the spatiotemporal encoder-decoder network using Convolutional LSTM (ConvLSTM) modules, which can be learned implicitly without any pixel-level annotations. By leveraging the spatiotemporal attention, we also formulate the 3D convolutional neural networks (3D-CNNs) to robustly recognize the dimensional emotion in facial videos. The experimental results show that our method can achieve the state-of-the-art results in dimensional emotion recognition with the highest concordance correlation coefficient (CCC) on RECOLA and AV+EC 2017 dataset.
Iris Recognition System: A Novel Approach for Biometric Authentication

The main issue of this decade is security. Nowadays, there are different authentication systems based on a password, card and biometric are present. Password and card based systems are the failure due to humans' poor judgment. The researchers suggest that the biometric is most secure and prominent solution for systems security. Among the biometric systems, iris-based biometric systems are more secured. In this proposed system, segmentation technique utilized shape, intensity and localization of the pupil and iris. The segmented region is converted into the rectangular region by normalization process. The use of Gray Level Cooccurrence Matrix (GLCM) and Hausdorff Dimension (HD) extracts the texture based features. The extracted features were classified by supervised SVM machine learning algorithm. The performance of the proposed system shows the robustness of the system.