Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the Morphometric Variation of Fruits and Phylogenetics of Elaeocarpus ganitrus (Woodenbegar) and Elaeocarpus serratus (Ceylon Olive)

Purpose : Elaeocarpus ganitrus and E. serratus are two significant tropical fruit tree species. E. ganitrus is also important for its endocarps that are popularly known as Rudraksha. However, these are underutilized species in Sri Lanka. Therefore, present study was conducted to assess the morphometric diversity of fruits and endocarps and the phylogenetic relationships of two species using DNA barcoding markers. Research Method : The ripe fruits were collected from a set of trees of two species in Matale and Kandy districts. The fruit and endocarp size parameters were measured in two seasons and subjected to statistical analysis. The trnH-psbA and trnL-trnF sequences were obtained from the selected trees and subjected to phylogenetic analysis. Findings : The fruit size traits were predominantly affected by the intraspecific variation than the environment. The length and diameter of the endocarps were seasonal independent and tree specific. Except colour, people preferred E. serratus fruits than E. ganitrus fruits. The markers, trnH-psbA and trnL-trnF unravelled the species delimits of E. ganitrus and E. serratus. The trnL-trnF based phylogeny positioned the trees of E. ganitrus in the respective clade. E. serratus trees were cladded with E. stipularis, E. tectorius, E. dongnaiensis and E. glaber. Research Limitations: A limited sequence pop-set for Elaeocarpus spp. is available in literature for comparison. The meta-scale attempts are needed to resolve the phylogenetic relationships of Elaeocarpus germplasm. Originality / Value : The revealed morphological and phylogenetic relationships of E. ganitrus and E. serratus lay the platform for germplasm conservation and crop improvement programs in Sri Lanka.
RECORDS OF PHARMACEUTICAL AND BIOMEDICAL SCIENCES Solid lipid nanoparticles: a prospective approach for topical drug delivery

Solid lipid nanoparticles (SLNs) are categorized as a novel generation of nanocarriers attracting huge attention as innovative colloidal carrier systems used for drug delivery as they integrate the advantages and avoid the disadvantages of other colloidal carries such as liposomes and polymeric nanoparticles. SLNs are biocompatible and biodegradable as they are made from physiological lipids, which decrease the danger of toxicity. Moreover, SLNs have been used as a possible attractive drugcarrier for topical drug delivery. These review discuses on many aspects of SLNs such as advantages disadvantages, method of preparation, SLNs and topical delivery and their therapeutic applications.
Flexible-Screen-Printed Antenna With Enhanced Bandwidth by Employing Defected Ground Structure

A flexible, wideband, and screen-printed antenna is proposed. The antenna is coplanar-waveguide and composed of two inverted L-shaped elements, a matching stub, and a defected ground structure (DGS), with a total size of 55 × 40 × 0.125 mm3. Studies show that the DGS can significantly increase the antenna bandwidth from 30% to 119% without sacrificing the size. The antenna has a wide measured bandwidth of 1.77–6.95 GHz, and a measured peak gain and efficiency in the ranges of 2.5–5.9 dBi and 60%–90%, respectively. Furthermore, both simulation and measurement confirm that the performance of the antenna is relatively stable under the bending conditions with various curvature radii.
Estimating the size of small populations from incomplete lists via graphical models

We consider the problem of estimating the size N of a closed population from q incomplete lists. Estimation of N is based on capture-recapture type models. We use graphical models to deal with possible dependencies between lists. The current parametriza-tion involves clique probabilities which have no simple concrete meaning and are delicate to manipulate in a Bayesian context insofar as hyper-Dirichlet distributions are used as priors. Our parametrization involves marginal and conditional capture probabilities. We develop our approach with q = 3. We show that there is a one-to-one and onto correspondence between both parametrizations and that placing hyper-Dirichlet distributions on the clique parameter boils down to place independent beta distributions on the capture parameters. When N is small, the non informative Bayesian analysis encounters difficulties. The posterior distribution of N may not exist for a particular graphical model: we give a necessary and sufficient condition of existence for each. Moreover, it is highly desirable that the priors on capture are compatible across the different models. Now, due to the small size of N , fulfilling this requirement demands a particular attention. We conclude by extending our approach to q = 4 lists.
Pathway reconstruction from combinatorial gene knockdowns exploiting siRNA off-target effects

Pathway reconstruction has proven to be an indispensable tool for analyzing the underlying molecular mechanisms of a cell. Nested effects models (NEMs) are a class of probabilistic graphical models, that have been designed to reconstruct pathways from high dimensional observations resulting from RNA interference (RNAi) experiments. NEMs assume that the short-interfering RNAs (siRNAs) designed to knockdown specific genes are always on-target. However, in reality most siRNAs exhibit strong off-target effects which further confound the RNAi screen data, thus resulting in inaccurate reconstruction of networks by NEMs. Here, we used an extension of NEMs called combinatorial nested effects models (c-NEMs) which capitalize on the ancillary siRNA off-target effects information for network reconstruction from combinatorial gene knockdown data. We investigated and delineated the possible siRNA off-target effects that favored feasible inference of three and four node networks using c-NEMs and further evaluated the identifiability of c-NEMs. An extensive simulation study examining the performance of network inference as a function of the number of effects and the number of off-targeted genes (per siRNA) demonstrated that c-NEMs improves the inference of networks over NEMs by utilizing the supplementary siRNA off-target effects information. Validation by reconstructing an eight node network from Bartonella henselae infection RNAi screen data showed a 15.06% improvement in performance by c-NEMs.
Localization of Indoor Mobile Networking

- Reliable indoor location techniques are essential for the development of advanced location-conscious applications. Most of the previously proposed solutions to this problem assume that the nodes can use some ranging technology to obtain pair distances to other nearby nodes. These techniques for indoor localization fix the inadequacy of the global positioning system within a closed setting, such as houses. This research describes and evaluates a method for locating devices that use a wireless network to communicate. The distances between a blind node, unable to decide its position, and a group of anchor nodes, recognizing its localization, are calculated using the signal attenuation (Relative Received Signal Strength Indicator) obtained while capturing International Mobile Subscriber Identity numbers. The position is calculated using the triangulation method.
Path‐based Monte Carlo Denoising Using a Three‐Scale Neural Network

Monte Carlo rendering is widely used in the movie industry. Since it is costly to produce noise‐free results directly, Monte Carlo denoising is often applied as a post‐process. Recently, deep learning methods have been successfully leveraged in Monte Carlo denoising. They are able to produce high quality denoised results, even with very low sample rate, e.g. 4 spp (sample per pixel). However, for difficult scene configurations, some details could be blurred in the denoised results. In this paper, we aim at preserving more details from inputs rendered with low spp. We propose a novel denoising pipeline that handles three‐scale features ‐ pixel, sample and path ‐ to preserve sharp details, uses an improved Res2Net feature extractor to reduce the network parameters and a smooth feature attention mechanism to remove low‐frequency splotches. As a result, our method achieves higher denoising quality and preserves better details than the previous methods.
Regulation of Telomerase by DNA and Protein Interactions

In most eukaryotes, chromosomes ends are protected by telomeres which are formed by repetitive DNA, specialized binding proteins, and higher order structures. Telomeres become shorter following replication due to the positioning and degradation of terminal RNA primers, as well as resection by nucleases. Extensive telomere shortening over many cell cycles elicits a DNA damage checkpoint that culminates in senescence or, in the absence of tumor suppressor pathways, apoptosis. These effects block the expansion of cells with unstable genomes, but can also precipitate disease in tissues that rely on regeneration for function. In many unicellular eukaryotes and proliferative human cells including cancer cells, telomeres can be maintained by the telomerase reverse transcriptase (TERT) and its associated RNA (TR). The elongation of telomeric DNA by telomerase depends on the telomerase essential N-terminal (TEN) and C-terminal reverse transcriptase (RT) domains. We found that human TEN interacted with singlestranded telomeric DNA and restored function, in trans, to an hTERT mutant lacking hTEN. Telomerase required hTEN residues for activity, telomere maintenance, and extension of cellular replicative lifespan. Two inactive hTERT variants bearing mutations in TEN and RT domains, respectively, cooperated to regenerate telomerase activity in vitro. hTEN interacted with several regions of hTERT suggesting that dimerization may occur via TEN-TERT interactions. The in vivo defect of certain hTEN mutants may involve an inability to interact with factors that recruit the enzyme to the telomere and/or stimulate activity. Human homologs of the S. cerevisiae recruitment factor Est1 interacted with telomerase in a
A Survey: Opinion Mining Its Tools and Techniques

: In previous era the very first step when someone is looking for buying a new product they take advice from relatives, friends or if a company need to launch a new product they perform surveys which was very time consuming task. But today all are busy in their life and have no time for this kind of stuff. Web being a wonderful substitution for individual opinion asking. Various sites provide different reviews about the product. But size of web is very large. This enormous data again increases the problem of users. So what we need is an automatic technique which provides only valuable information which a user actually requires. Opinion mining is that technique which provides exact opinion from such enormous data about a specific product. It has become the helping hand of today’s generation for private decisions from public means. In this paper we discuss about opinion mining, its types, various tools for accomplishing opinion mining task, and its challenges.
Notes on the Harpy Eagle in British Guiana

AnT•OUG• the Harpy Eagle (Harpia harpyja) is the heaviest and most powerful of all eagles, a truly dramatic bird, little has been published concerning its life history. Most accounts appeared prior to 1900 and are largely anecdotal. The species has a wide range extending throughout tropical America from southern Mexico to northern Argentina but is nowhere numerous. Chubb (1916) quotes from the works of Schomburgk (1848), Quelch (1890), Lloyd (1897), and Brown (1876), in briefly describing its habits and general distribution in British Guiana. Bond (1927) observed a nest in northeastern Brazil and took from it two eggs, 12 days apart, the second perhaps a replacement. In British Guiana, from December, 1959, to May, 1960, we made many observations at the nest sites of two families of Harpy Eagles, either from a blind or from other concealment in the immediate vicinity of their nest trees. We took precaution at all times to keep the eagles from being aware of our presence. We think these are among the first detailed observations of Harpy Eagles in their native habitat. Eventually a juvenile male (Figure 1) was trapped at one nest site and an adult female (Figure 2) and a juvenile female at the other. Both juvenile birds were in identical plumage and were judged to be between 8 and 10 months old when trapped. All three eagles were subsequently trained to have little fear of humans, and thus they could be studied at close range. As of May, 1963, they were still being maintained successfully in captivity and observations on plumage and food requirements were continuing. Although much of the behavior of a trained eagle closely approximates the behavior of a wild eagle, behavior of captive birds is designated as such in the text.
Strategy Games: The Components of A Worthy Opponent

What distinguishes a good AI opponent from a bad one in the eyes of players? To answer this question, hundreds of opinions were analysed, as expressed by strategy-game players in forums; from these, a grounded theory was formed. It was found that the AI’s role as an opponent in a game shapes player expectations, and that not breaking these is instrumental to players’ enjoyment. The expectations include: keeping a tension between the player and AI; maintaining a level playing field; and more subtle expectations involving closure and the ability to rectify behaviour. The nature of these expectations are explored, as well as how they might be upheld.
Djehuty: A Mixed-Initiative Handwriting Game for Preschoolers

Learning to read and write is a fundamental right and a necessary skill for the personal, cultural, and economic development of people and their societies. However, children of developing countries, such as sub-Saharan areas, are currently at a greater risk of illiteracy. The current penetration of mobile technologies and the internet in sub-Saharan rural areas, however, offers a unique opportunity for tackling the challenge of literacy at a large scale. Motivated by the current shortage of preschool teachers for training handwriting in a personalised manner, this paper discusses the design of Djehuty, an educational gamified environment for preschoolers. Djehuty is equipped with an artificial intelligence module which generates a style of handwriting and suggests handwriting paths to the child in a mixed-initiative manner. The paper presents the key elements of the game prototype.
Intensifying Stress Perception Using Visual Effects in VR Games

Visual effects are used in commercial video games to enhance the players’ perception without being investigated in terms of their actual impact on the players. In our work, we implemented five visual effects, namely vignetting, image noise, chromatic aberration, color grading, and blur and investigated their impact on the players’ perceived stress, presence, and simulator sickness. A user study (N = 6), conducted with a virtual reality (VR) head-mounted display (HMD), shows that these visual effects can intensify the players’ stress perception without negatively effecting perceived presence and without increasing simulator sickness.
Multi-Modal Study of the Effect of Time Pressure in a Crisis Management Game

In this paper, we study the effect of time pressure on player behaviour during a dilemma-based crisis management game. We employ in-game action tracking, physiological sensor data and self-reporting in order to create multi-modal predictive models of player stress responses during a crisis management scenario. We were able to predict the experimental condition (time pressure vs. no time pressure) with 84.5% accuracy, using a game-only feature set. However, lower accuracy was observed when physiological sensor data was used for the same task. The method presented in this paper can be employed in crisis management training, aiming at assessing players’ responses to stressful conditions and manipulating player stress levels to provide personalised training scenarios.
Micro-level examination of games using Indicator Analysis

Current tools and methods for game design evaluation often focus on how to create good game design, but end up being prescriptive. In this paper we present the Indicator Analysis method - consisting of a theoretical framework, an analysis method, and a model aimed at avoiding bad game design by examining the impact of details, called indicators, found in the game. We also show how to apply this theoretical framework and model to perform game design evaluation, and what types of questions can be answered by applying them. This approach is relevant due to the increased use of artificial intelligence technologies in games, where the focus in inherently on details.
Gibbs-Slice Sampling Algorithm for Estimating the Four-Parameter Logistic Model

The four-parameter logistic (4PL) model has recently attracted much interest in educational testing and psychological measurement. This paper develops a new Gibbs-slice sampling algorithm for estimating the 4PL model parameters in a fully Bayesian framework. Here, the Gibbs algorithm is employed to improve the sampling efficiency by using the conjugate prior distributions in updating asymptote parameters. A slice sampling algorithm is used to update the 2PL model parameters, which overcomes the dependence of the Metropolis–Hastings algorithm on the proposal distribution (tuning parameters). In fact, the Gibbs-slice sampling algorithm not only improves the accuracy of parameter estimation, but also enhances sampling efficiency. Simulation studies are conducted to show the good performance of the proposed Gibbs-slice sampling algorithm and to investigate the impact of different choices of prior distribution on the accuracy of parameter estimation. Based on Markov chain Monte Carlo samples from the posterior distributions, the deviance information criterion and the logarithm of the pseudomarginal likelihood are considered to assess the model fittings. Moreover, a detailed analysis of PISA data is carried out to illustrate the proposed methodology.
Editorial: Metacognitive Therapy: Science and Practice of a Paradigm

1 Faculty of Biology Medicine and Health, School of Psychological Sciences, University of Manchester, Manchester, United Kingdom, 2 Research and Innovation, Greater Manchester Mental Health National Health Service Foundation Trust, Manchester, United Kingdom, 3 Institute for Simulation and Training, University of Central Florida, Orlando, FL, United States, Department of Mental Health, Norwegian University of Science and Technology, Trondheim, Norway
Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review

Manipulation of deformable objects has given rise to an important set of open problems in the field of robotics. Application areas include robotic surgery, household robotics, manufacturing, logistics, and agriculture, to name a few. Related research problems span modeling and estimation of an object's shape, estimation of an object's material properties, such as elasticity and plasticity, object tracking and state estimation during manipulation, and manipulation planning and control. In this survey article, we start by providing a tutorial on foundational aspects of models of shape and shape dynamics. We then use this as the basis for a review of existing work on learning and estimation of these models and on motion planning and control to achieve desired deformations. We also discuss potential future lines of work.
Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks

Up to 40% of very preterm infants (≤32 weeks’ gestational age) were identified with a cognitive deficit at 2 years of age. Yet, accurate clinical diagnosis of cognitive deficit cannot be made until early childhood around 3–5 years of age. Recently, brain structural connectome that was constructed by advanced diffusion tensor imaging (DTI) technique has been playing an important role in understanding human cognitive functions. However, available annotated neuroimaging datasets with clinical and outcome information are usually limited and expensive to enlarge in the very preterm infants’ studies. These challenges hinder the development of neonatal prognostic tools for early prediction of cognitive deficit in very preterm infants. In this study, we considered the brain structural connectome as a 2D image and applied established deep convolutional neural networks to learn the spatial and topological information of the brain connectome. Furthermore, the transfer learning technique was utilized to mitigate the issue of insufficient training data. As such, we developed a transfer learning enhanced convolutional neural network (TL-CNN) model for early prediction of cognitive assessment at 2 years of age in very preterm infants using brain structural connectome. A total of 110 very preterm infants were enrolled in this work. Brain structural connectome was constructed using DTI images scanned at term-equivalent age. Bayley III cognitive assessments were conducted at 2 years of corrected age. We applied the proposed model to both cognitive deficit classification and continuous cognitive score prediction tasks. The results demonstrated that TL-CNN achieved improved performance compared to multiple peer models. Finally, we identified the brain regions most discriminative to the cognitive deficit. The results suggest that deep learning models may facilitate early prediction of later neurodevelopmental outcomes in very preterm infants at term-equivalent age.
Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals

Recent statistics have shown that the main difficulty in detecting alcoholism is the unreliability of the information presented by patients with alcoholism; this factor confusing the early diagnosis and it can reduce the effectiveness of treatment. However, electroencephalogram (EEG) exams can provide more reliable data for analysis of this behavior. This paper proposes a new approach for the automatic diagnosis of patients with alcoholism and introduces an analysis of the EEG signals from a two-dimensional perspective according to changes in the neural activity, highlighting the influence of high and low-frequency signals. This approach uses a two-dimensional feature extraction method, as well as the application of recent Computer Vision (CV) techniques, such as Transfer Learning with Convolutional Neural Networks (CNN). The methodology to evaluate our proposal used 21 combinations of the traditional classification methods and 84 combinations of recent CNN architectures used as feature extractors combined with the following classical classifiers: Gaussian Naive Bayes, K-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM). CNN MobileNet combined with SVM achieved the best results in Accuracy (95.33%), Precision (95.68%), F1-Score (95.24%), and Recall (95.00%). This combination outperformed the traditional methods by up to 8%. Thus, this approach is applicable as a classification stage for computer-aided diagnoses, useful for the triage of patients, and clinical support for the early diagnosis of this disease.