Miracle-dz/UniFAR-FTUs huggingface dataset:
Phytochemical and Phytotherapeutic Properties of Celosia species- A Review

Medicinal plants have been used basically in all cultures as a source of medicine. Medicinal plants play an important role in public health, especially in developed and developing countries and leads to examination for therapeutic and pharmacological applications. Medicinal Plants have been used for thousands of years to flavor and conserve food, to treat different health issues and to prevent diseases including epidemics. Active compounds produced during secondary metabolism are usually posses the biological properties of plant species used throughout the globe for various purposes, including treatment of infectious diseases. Based on the medicinal values, the Celosia species has been chosen for this study. Celosia species plays an important role in traditional medicine to cure several disorders such as fever, diarrohea, mouth sores, itching, wounds, jaundice, gonorrhea, and inflammation. A variety of phytoconstituents are isolated from the Celosia species which includes triterpenoids, saponins, alkaloids, phenols, tannins, flavonoids, cardiac glycosides, steroids, phytosterols, and phlobatannins. The plant having potential pharmacological values screened for its various pharmacological activities, namely, anti-inflammatory, immune-stimulating, anticancer, hepatoprotective, antioxidant, wound healing, antidiabetic, antinociceptive effect and antibacterial activities which are reported in the extracts of different parts of the plant. The objective of this study was to provide an overview of the ecology, phytoconstituents, pharmacology of Celosia species and to confess their therapeutic potentials and secure an evidence base for further research works on Celosia.
Evaluating the Emotional State of a User Using a Webcam

In online learning is more difficult for teachers identify to see how individual students behave. Student’s emotions like self-esteem, motivation, commitment, and others that are believed to be determinant in student’s performance can not be ignored, as they are known (affective states and also learning styles) to greatly influence student’s learning. The ability of the computer to evaluate the emotional state of the user is getting bigger attention. By evaluating the emotional state, there is an attempt to overcome the barrier between man and non-emotional machine. Recognition of a real time emotion in e-learning by using webcams is research area in the last decade. Improving learning through webcams and microphones offers relevant feedback based upon learner’s facial expressions and verbalizations. The majority of current software does not work in real time – scans face and progressively evaluates its features. The designed software works by the use neural networks in real time which enable to apply the software into various fields of our lives and thus actively influence its quality. Validation of face emotion recognition software was annotated by using various experts. These expert findings were contrasted with the software results. An overall accuracy of our software based on the requested emotions and the recognized emotions is 78%. Online evaluation of emotions is an appropriate technology for enhancing the quality and efficacy of e-learning by including the learner´s emotional states.
Restraining and encouraging the use of hand gestures: Effects on speech

Authors acknowledge funding from the University of Padua’s Doctoral School of Linguistic, Philological and Literary Sciences, the Spanish Ministry of Science and Innovation (grant FFI2015-66533-P), and a grant awarded by the Generalitat de Catalunya (2014SGR-925) to the Prosodic Studies Group.
Z-scan Technique for Measurements of Glucose Concentrations through the Formation of Plasmonic Gold Nanoparticles Prepared by Green Synthesis

Gold nanoparticles were synthesized by microwave irradiation using glucose as a reducing and stabilizing agent. The effects of using different concentrations of glucose in the synthesis of nanoparticles were investigated. The formation of gold nanoparticles was confirmed using UV-Vis absorption spectra and Transmission Electron Microscope(TEM). Z-scan technique was used to measure the nonlinear refraction indices of the gold nanoparticlessynthesized with different glucose concentrations. A linear relationship was found between the nonlinear refractive index of the total gold-glucose and concentration. Thus the calculated values of the nonlinear refractive index may be used for measuring an unknown glucose concentration in a sample.
Neurocalcin δ Modulation of ROS-GC1, a New Model of Ca 2+ Signaling †

ROS-GC1 membrane guanylate cyclase is a Ca 2+ bimodal signal transduction switch. It is turned “off” by a rise in free Ca 2+ from nanomolar to the semicromolar range in the photoreceptor outer segments and the olfactory bulb neurons; by a similar rise in the bipolar and ganglion retinal neurons it is turned “on”. These opposite operational modes of the switch are specified by its Ca 2+ sensing devices, respectively termed GCAPs and CD-GCAPs. Neurocalcin δ is a CD-GCAP. In the present study, the neurocalcin δ -modulated site, V 837 –L 858 , in ROS-GC1 has been mapped. The location and properties of this site are unique. It resides within the core domain of the catalytic module and does not require the α -helical dimerization domain structural element (amino acids 767–811) for activating the catalytic module. Contrary to the current beliefs, the catalytic module is intrinsically active; it is directly regulated by the neurocalcin δ -modulated Ca 2+ signal and is dimeric in nature. A fold recognition based model of the catalytic domain of ROS-GC1 was built, and neurocalcin δ docking simulations were carried out to define the three-dimensional features of the interacting domains of the two molecules. These findings define a new transduction model for the Ca 2+ signaling of ROS-GC1. to modeling docking as the parameters the of the model and docked complexes, regarding the model respect to its substrate- (GTP-) binding site and dimerization, and analyses regarding the ROS-GC1 neurocalcin δ the
Enzymatic Conversion of Sex Pheromones in Olfactory Hairs of the Male Silkworm Moth Antheraea polyphemus

Abstract The sex pheromone compound [6,7-3H]-6,ll-hexadecadienyl acetate is enzymatically converted to its corresponding fatty alcohol, aldehyde and acid in living and homogenized olfactory hairs and in other parts of the antennae of the male silk-moth Antheraea polyphemus. The pheromone is metabolized in vitro mainly in the inner hydrophilic compartments, but scarcely in the outer hydrophobic cuticle of the olfactory hairs.
Predicting the Probability Density Function of Music Emotion Using Emotion Space Mapping

Computationally modeling the affective content of music has been intensively studied in recent years because of its wide applications in music retrieval and recommendation. Although significant progress has been made, this task remains challenging due to the difficulty in properly characterizing the emotion of a music piece. Music emotion perceived by people is subjective by nature and thus complicates the process of collecting the emotion annotations as well as developing the predictive model. Instead of assuming people can reach a consensus on the emotion of music, in this work we propose a novel machine learning approach that characterizes the music emotion as a probability distribution in the valence-arousal (VA) emotion space, not only tackling the subjectivity but also precisely describing the emotions of a music piece. Specifically, we represent the emotion of a music piece as a probability density function (PDF) in the VA space via kernel density estimation from human annotations. To associate emotion with the audio features extracted from music pieces, we learn the combination coefficients by optimizing some objective functions of audio features, and then predict the emotion of an unseen piece by linearly combining the PDFs of the training pieces with the coefficients. Several algorithms for learning the coefficients are studied. Evaluations on the NTUMIR and MediaEval 2013 datasets validate the effectiveness of the proposed methods in predicting the probability distributions of emotion from audio features. We also demonstrate how to use the proposed approach in emotion-based music retrieval.
A Review on Presentation Slides Generation for Scientific Papers

: This paper discusses a way for slides generation without human intervention from a text, studying the generation of presentation slides from a scientific paper and also examines the demanding task of incessantly creating presentation slides from scientific papers. The produced slides can be used as a rough copy to help moderators setup their orderly slides in a swift manner. This paper introduces a novel system called PPSGen to assist everyone create such slides. The system uses the revert procedure to conclude the Score of the sentences in an paper and then uses the whole number Integer Linear Programming (ILP) system to create well-ordered slides by selecting and changing key vocabulary and sentences. Results are based on a test game plan of 200 arrangements of papers and slides assembled on the web, display that our proposed PPSGen structure can make slides with improved quality. A client study also exhibits that PPSGen has palpable benefit over baseline methods.
A parallel discrete surface integral equation method for the analysis of three-dimensional microwave circuit devices with planar symmetry

It has been found that the discrete integral equation (DSI) technique is a highly effective technique for the analysis of microwave circuits and devices. In the present paper, a technique that exploits planar symmetries is described, namely, planar symmetry is recognized for three-dimensional geometries which can be uniquely described by a projection onto a two-dimensional plane. To this end, the three-dimensional model is described by a two-dimensional grid. Furthermore, due to regularity along the third-dimension the sparse matrices used for the field updates need only be computed for the two-dimensional grid. This results in a significant savings in storage, increasing potential problem sizes that can be solved by orders of magnitude. Based on the planar DSI algorithm, a highly scalable parallel algorithm is presented. The parallel algorithm is based on a spatial discretization of the two-dimensional mesh. By treating the update matrices as a subassembly of matrices, the matrix-vector multiplications are easily done in parallel requiring only minimal interprocessor communication. Finally, the robustness and efficiency of the algorithm is illustrated through a number of examples.
Analytical models for the mean flow inside dense canopies on gentle hilly terrain

Simplifications and scaling arguments employed in analytical models that link topographic variations to mean velocity perturbations within dense canopies are explored using laboratory experiments. Laser Doppler anemometry (LDA) measurements are conducted in a neutrally‐stratified boundary‐layer flow within a large recirculating flume over a train of gentle hills covered by a dense canopy. The hill and canopy configuration are such that the mean hill slope is small and the hill is narrow in relation to the canopy (H/L ≪ 1 and Lc/L ≈ 1, where H is the hill height, L the half‐length, and Lc the canopy adjustment length‐scale). The LDA data suggest that the often‐criticized linearizations of the advective terms, turbulent‐shear‐stress gradients and drag force appear reasonable except in the deep layers of the canopy. As predicted by a previous analytical model, the LDA data reveal a recirculation region within the lower canopy on the lee slope. Adjusting the outer‐layer pressure perturbations by a virtual ground that accounts for the mean streamline distortions induced by this recirculation zone improves this model's performance. For the velocity perturbations in the deeper layers of the canopy, a new analytical model, which retains a balance between mean horizontal advection, mean pressure gradient and mean drag force but neglects the turbulent‐shear‐stress gradient, is developed. The proposed model reproduces the LDA measurements better than the earlier analytical model, which neglected advection but retained the turbulent‐shear‐stress gradient in the lower layers of the canopy and near the hill top. This finding is consistent with the fact that the earlier model was derived for tall hills in which advection inside the canopy remains small. In essence, the newly‐proposed model for the narrow hill studied here assumes that in the deeper layers of the canopy the spatial features of the mean flow perturbations around their background state can be approximated by the inviscid mean‐momentum equation. We briefly discuss how to integrate all these findings with recent advances in canopy lidar remote‐sensing measurements of general topography and canopy height. Copyright © 2008 Royal Meteorological Society
EFFECT OF METHANOLIC LEAF EXTRACT AND ETHYL ACETATE FRACTION OF BUTEA MONOSPERMA ON β-AMYLOID ( 25-35 ) INDUCED AMNESIA IN LABORATORY ANIMALS

Alzheimer’s disease (AD), mainly caused by the accumulation of β-amyloid (Aβ), increased acetylcholinesterase enzyme (AChE) and monoamino oxidase (MAO) with reduced turnover of biogenic amines. Oxidative stress in AD induces elevation of corticosterone and produce subsequent neuroimmune changes. Main objective of this study was to assess the pharmacological activity of B. monosperma leaf (methanolic extract and ethyl acetate fraction) on βamyloid peptide induced amnesia in mice. Currently, available drugs to treat AD like AChE inhibitors exert symptomatic relief but do not reduce progression of disease and have side effects. Hence, herbal medicines may provide potential effect as compared to conventional available synthetic drugs with less or no side effects. The methanolic extract and its fraction are subjected to jumping test, rectangular maze test, Y-maze test and biochemical estimations such as AChE, peroxidase, MDA and DPPH radical scavenging activity. From the above results, it was found that both methanol extract and ethyl acetate fraction of B. monosperma are having significant anti-amnesic activity and anti-oxidant activity. Ethyl acetate fraction is having more significant activity when compared to methanol extract. By identifying and isolating the chemical constituents responsible for anti-amnesic and antioxidant activities, we may find a breakthrough in the treatment of other neurodegenerative disorders which occur due to oxidative stress.
Estimation of canopy height using an unmanned aerial vehicle in the field during wheat growth season

Canopy height is a simple trait to represent status of plant growth and development and potentially biomass production, as well as having an influence on lodging susceptibility. Measurement is labour-intensive and time consuming, especially in the large breeding trials, which involve thousands of plots. In this study, we developed a methodology to estimate canopy height using a high resolution camera which was mounted on an Unmanned Aerial Vehicle (UAV). Aerial images were captured during a wheat growth season with contrasting canopy height at four time points. Digital surface models (DSMs) were generated using structure from motion (SfM) algorithm after 3D reconstruction of the whole field (e.g. phenocopter.csiro.au). Three methods were used to estimate soil surface and canopy heights at plot level after segmentation of DSM into individual plots including 1) background extraction after segmentation mosaic into vegetation; 2) interpolation of soil surface using soil surface around field edges; 3) measured soil DSM by a flight immediately after planting. Estimates of canopy heights were compared to manual measurements close to the flight times. The results indicated Method 2 had the best performance for wheat canopy (R = 0.88; RMSE = 4.5 cm). The proposed method can been integrated into the high throughput phenotyping platform to apply UAV technologies in the breeding program.
Optimizing Weighted Ensemble Sampling of Steady States

We propose parameter optimization techniques for weighted ensemble sampling of Markov chains in the steady-state regime. Weighted ensemble consists of replicas of a Markov chain, each carrying a weight, that are periodically resampled according to their weights inside of each of a number of bins that partition state space. We derive, from first principles, strategies for optimizing the choices of weighted ensemble parameters, in particular the choice of bins and the number of replicas to maintain in each bin. In a simple numerical example, we compare our new strategies with more traditional ones and with direct Monte Carlo.
Vuckovic, A. and Hasan, M.A. and Osuagwu, B. and Fraser, M. and Allan, D.B. and Conway, B.A. and Nasseroleslami, B. (2015) The influence of central neuropathic pain in paraplegic patients on performance of a motor imagery based brain

Objective: The aim of this study was to test how the presence of central neuropathic pain (CNP) influences the performance of a motor imagery based Brain Computer Interface (BCI). Methods: In this electroencephalography (EEG) based study, we tested BCI classification accuracy and analysed event related desynchronisation (ERD) in 3 groups of volunteers during imagined movements of their arms and legs. The groups comprised of nine able-bodied people, ten paraplegic patients with CNP (lower abdomen and legs) and nine paraplegic patients without CNP. We tested two types of classifiers: a 3 channel bipolar montage and classifiers based on common spatial patterns (CSPs), with varying number of channels and CSPs. Results: Paraplegic patients with CNP achieved higher classification accuracy and had stronger ERD than paraplegic patients with no pain for all classifier configurations. Highest 2-class classification accuracy was achieved for CSP classifier covering wider cortical area: 82 ± 7% for patients with CNP, 82 ± 4% for able-bodied and 78 ± 5% for patients with no pain. Conclusion: Presence of CNP improves BCI classification accuracy due to stronger and more distinct ERD. Significance: Results of the study show that CNP is an important confounding factor influencing the performance of motor imagery based BCI based on ERD. 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The Interplay between Classical Aesthetics, Expressive Aesthetics and Persuasiveness in Behavior Modeling

Research has shown that visual aesthetics plays a key role in the persuasiveness of behavior change support systems. However, there is limited knowledge on how much each of the two dimensions of visual aesthetics ( classical and expressive ) contributes to the aesthetics - persuasiveness relationship. To bridge this gap, we conducted an empirical study among 669 subjects in North America to investigate: (1) the interrelationships among all three design constructs— classical aesthetics , expressive aesthetics and persuasiveness —using fitness apps featuring exercise behavior models as a case study; and (2) the moderating effect of the gender, race and exercise-type characteristics of the behavior model. Our Structure Equation Model (SEM) analysis shows that, regardless of the gender, race and exercise-type characteristics of the behavior model, both classical aesthetics and expressive aesthetics significantly impact persuasiveness . Overall, our path model explains 41% of the variance of persuasiveness , with expressive aesthetics having a stronger direct influence and mediating the influence of classical aesthetics on persuasiveness . Our findings underscore the need for designers of persuasive apps to leverage both dimensions of visual aesthetics —especially expressive aesthetics —in the design of behavior models to increase their persuasiveness as change agents.
Efficient Training of Small Kernel Convolutional Neural Networks using Fast Fourier Transform

Highlander, Tyler. M.S., Department of Computer Science and Engineering, Wright State University, 2015. Efficient Training of Small Kernel Convolutional Neural Networks using Fast Fourier Transform. Convolutional neural networks (CNNs) are currently state-of-the-art for various classification tasks, but are computationally expensive. Propagating through the convolutional layers is very slow, as each kernel in each layer must sequentially calculate many inner products for a single forward and backward propagation which equates to O(Nn) per kernel per layer where the inputs are N ×N arrays and the kernels are n× n arrays. Convolution can be efficiently performed as a Hadamard product in the frequency domain. The bottleneck is the transformation which has a cost of O(N log2N) using the fast Fourier transform (FFT). However, the increase in efficiency is less significant when N n as is the case in CNNs. We mitigate this by using the “overlap-and-add” technique reducing the computational complexity to O(N log2 n) per kernel. This method increases the algorithm’s efficiency in both the forward and backward propagation, significantly reducing the training and testing time for CNNs. Our empirical results show our method reduces computational time by a factor of up to 50.4 times the traditional convolution implementation.
Assessment of Bovine Tuberculosis like Lesions and Its Risk Factors in Cattle Slaughtered at Hawassa University and Municipal Abattoirs , Southern Ethiopia

Background: a cross sectional study was conducted on 753 Cattle, selected using systematic random sampling technique, in Hawassa abattoirs, Southern Ethiopia from December 2015 to May 2016 to investigate the occurrence of Bovine Tuberculosis in animals slaughtered at Hawassa University and Municipal Abattoirs, and to identifying the potential risk factors, Southern Ethiopia. The methods used were ante mortem, to identifying the risk factors, and postmortem examinations, to determining the bovine tuberculosis like lesions. Result: the overall prevalence of the bovine tuberculosis was 5.8% (95% CI: 4.16-7.52) on the basis of detailed postmortem examination. Multivariable logistic regression analysis identified age, body condition; breed and market were statistically significant. The older cattle above 8 years were eighteen times (OR = 18.25) more likely to have tuberculosis than the younger cattle. The cross breeds were nine times (OR = 9.75) more sensitive to M. bovis as compared to local breeds as well as the poor body conditioned cattle forty-one times (OR = 41.21) more likely to have tuberculosis than the cattle have good body condition. Relatively, the occurrence of BTB was higher in cattle brought from Hawassa (OR = 13.17) and Borena (OR = 9.41) markets than Tula market. The lesions were found most frequently distributed in thoracic cavity lymph nodes (75%), which indicated that respiratory route was the main mode of infection in the study area. Conclusion: Using the gross pathological lesion this study shows that low overall prevalence of bovine tuberculosis in sampled animals. Yibrah Tekle1*, Gezahegn Mamo2, Gobena Ameni3 and Mulugeta Ftiwi1 11College of Agriculture & Natural Resources, Department of Animal Science, Raya University, Maichew, Ethiopia 2College of Veterinary Medicine and Agriculture, Addis Ababa University, Bishoftu, Ethiopia 3Aklilu Lemma Institute of Pathobiology, Addis Ababa University, Addis Ababa, Ethiopia *Address for Correspondence Yibrah Tekle, College of Agriculture and Natural Resources, Raya University, Maichew, Ethiopia, E-mail: goytomtekle@gmail.com Submission: March 07, 2017 Accepted: August 31, 2017 Published: September 07, 2017 Copyright: © 2017 Yibrah Tekle, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Research Article Open Access Journal of Veterinary Science & Medicine