Miracle-dz/UniFAR-FTUs huggingface dataset:
Physicochemical stability of grape juice produced on industrial scale by different commercial enzyme preparations

The present work evaluated the analytical characteristics, total phenolic contents (TPC), antioxidant activities (AOX), sugars and organic acids by RP-HPLC/DAD/RID of grape juices produced on an industrial scale, with three different commercial pectinases. They were evaluated by colour, total monomeric anthocyanins (TMA) and AOX of the juices stored for six months. Newly produced juices did not present differences in the classical parameters such as pH, °Brix, titratable acidity, colour and yield. There was no difference in the organic acids profile of the obtained juices. The juice obtained with predominant presence of hemicellulases presented rhamnose in its composition, indicating degradation of polysaccharides of the grape peel. The juices showed no difference in TPC and AOX between the studied pectinases. In storage studies, there were fast colour evolution, TMA and AOX in all samples. The juice obtained with the predominance of hemicellulases presented more accelerated loss of TMA and AOX. The results obtained in the present work demonstrated the importance of knowing the influence of the use of commercial pectinases in the process of producing grape juice, not only at the time of production, but during its shelf life. The comparison of the different pectinases used under real processing conditions showed that choice of enzymes used in the preparation could be decisive for the shelf life after bottling the product.
A Pilot Study of a Medical Cannabis - Opioid Reduction Program

Many chronic pain patients are prescribed opioids at doses exceeding the current Guideline. Tapering the dose can be difficult, as patients fear a return to a state of overwhelming pain. Several factors can increase the likelihood of success: the patient’s readiness for change, psychological support, pharmacological support and careful monitoring. This pilot study addressed these four factors. Six hundred patients took part. Each was taking daily opioid doses ranging from 90-240 mg morphine equivalent dose (MED). All indicated they were prepared to reduce their opioid dose. Over a six-month period, opioid doses were tapered according to individual needs, usually 10% every 1-2 weeks. Psychological support was provided through a freely available web-based mental health and wellness tool. Medical cannabis provided pharmacological support at the rate of 0.5g/day for each 10% reduction in opioid dose, as needed. Physicians monitored patients regularly according to each patient’s needs. After 6 months, 156 patients (26%) had ceased taking opioids. An additional 329 patients (55%) had reduced their opioid use by an average of 30%. One hundred fourteen patients (19%) neither increased nor decreased their opioid use. The one patient whose opioid dose was increased had poorly controlled pain and an aggravated pain condition. The success of this medical cannabis – opioid reduction program in a large proportion of patients is grounds for further investigation.
The tourism sharing economy and sustainability in developing countries : Contribution to SDGs in the hospitality sector

The tourism sharing economy applies the latest technological applications to monetize excess capacity and to provide ease of access to tourism resources. This concept is premised on the idea of collaborative consumption which emphasizes the importance of access rather than ownership. Homestays through Peer to Peer (P2P) and Business to Consumer (B2C) platforms are on the rise in the global tourism industry and the impacts of this phenomenon on sustainability are significant. In this era of Sustainable Development Goals (SDGs) it becomes important for accommodation providers to leverage off business models that contribute to sustainability. This study is based on a review of literature and addresses how the hospitality sector in developing countries can leverage on the sharing economy to contribute to the achievement of SDGs. The findings suggest that the sharing economy is still in its infancy in sub-Saharan African countries. The extant literature points to the fact that accommodation sharing can contribute to eight of the SDGs, namely 1, 4, 8, 9, 11, 12, 13 and 16.
Artificial neural networks and wavelet analysis in bio-radiolocation signal breathing patterns classification

A new approach for bio-radiolocation signal breathing patterns classification, based on artificial neural networks and wavelet analysis technique utilization, was proposed. Patterns attribute space is formed utilizing series of absolute values of wavelet decomposition detail coefficients of signal quadrature components. A multilayer perceptron with a backpropogation training algorithm and a bipolar sigmoid activation function of neurons was applied as a classifier. Analysis of efficiency of the method, carried out on bio-radiolocation signal models, revealed high classification accuracy on test sample of breathing patterns corresponding to three different classes: obstructive sleep apnea, central sleep apnea and normal calm sleeping.
Chapter 2 Improved Störmer – Verlet Formulae with Applications

The Störmer–Verlet formula is a popular numerical integration method which has played an important role in the numerical simulation of differential equations. In this chapter, we analyse two improved multi-frequency Störmer–Verlet formulae with four applications, including time-independent Schrödinger equations, wave equations, orbital problems and the problem of Fermi, Pasta and Ulam. Stability and phase properties of the two improved Störmer–Verlet formulae are analysed. In order to derive the first improved multi-frequency Störmer–Verlet formula, the symplectic conditions for the one-stage explicit multi-frequency ARKN method are investigated in detail. Moreover, the coupled conditions for explicit symplectic and symmetric multi-frequency ERKN integrators are presented.
Title Casein kinase 1 family regulates PRR 5 and TOC 1 in theArabidopsis circadian clock

Author(s) Uehara, Takahiro N. ; Mizutani, Yoshiyuki; Kuwata, Keiko; Hirota, Tsuyoshi; Sato, Ayato; Mizoi, Junya; Takao, Saori; Matsuo, Hiromi; Suzuki, Takamasa; Ito, Shogo; Saito, Ami N. ; Nishiwaki-Ohkawa, Taeko; Yamaguchi-Shinozaki, Kazuko; Yoshimura, Takashi; Kay, Steve A. ; Itami, Kenichiro; Kinoshita, Toshinori; Yamaguchi, Junichiro; Nakamichi, Norihito Citation Proceedings of the National Academy of Sciences (2019), 116(23): 11528-11536
Genetic variability evaluation and cultivar identification of tetraploid annual ryegrass using SSR markers

Annual ryegrass (Lolium multiflorum) is a widely used cool-season turf and forage grass with high productivity and ornamental characteristics. However, the abundant intra-cultivar genetic variability usually hampers the application of conventional techniques for cultivar identification. The objectives of this study were to: (1) describe an efficient strategy for identification of six tetraploid annual ryegrass cultivars and (2) investigate the genetic diversity based on SSR markers. A total of 242 reliable bands were obtained from 29 SSR primer pairs with an average of 8.3 bands for each primer pair and the average value of polymorphic information content (PIC) was 0.304. The result of analysis of molecular variance (AMOVA) revealed that 81.99% of the genetic variation occurred in within-cultivars and 18.01% among-cultivars. The principal coordinate analysis (PCoA) showed that the first two principal axes explain 8.57% (PC1) and 6.05% (PC2) of total variation, respectively. By using multi-bulk strategy based on different filtering thresholds, the results suggested that bands frequency of 40% could be used as a reliable standard for cultivar identification in annual ryegrass. Under this threshold, 12 SSR primer pairs (00-04A, 02-06G, 02-08C, 03-05A, 04-05B, 10-09E, 12-01A, 13-02H, 13-12D, 14-06F, 15-01C and 17-10D) were detected for direct identification of six tetraploid annual ryegrass cultivars, which could be incorporated into conservation schemes to protect the intellectual property of breeders, ensure purity for consumers, as well as guarantee effective use of cultivars in future.
Docking study of secondary metabolites from Glycyrrhiza glabra as PPAR-γ agonist

Discovery of potent yet cheap antidiabetic drugs with PPAR-γ agonist activity resulted in the discovery of several partial agonists of PPAR-γ. Glabridin, an isoflavone metabolite found in the root of Glycyrrhiza glabra has been associated with a wide range of biological properties, such as antidiabetic and antiobesity properties mediated by PPAR-γ receptors activation. This study aims to determine secondary metabolites of G. glabra with the highest affinity as PPAR-γ agonists with molecular docking method. The docking results showed that among other test ligands, the highest affinity was shown by glabrene. However, significant differences occur at amino acid residues from glabrene against the corresponding ligand. Interestingly, besides glabridin with 73% similar amino acid, the similarity of amino acid throughout the test ligands is less than half of glabridin. Glabridin also shows different types of interactions with some thiazolidinediones which are known to have quite dangerous side effects, so that glabridin is predicted not to have similar side effects. Thus, glabridin should be potential to be developed as the PPAR-γ agonist.
ctivity of Fabaceae species extracts against fungi and Leishmania : atacarpan as a novel potent anti-Candida agent andara

Leishmaniasis and fungal infection treatment efficacy is limited by toxicity and ever increasing resistance to available drugs, requiring development of alternative compounds. The richness of Cerrado plant antimicrobial secondary metabolites justifies screening of Fabaceae species extracts: Enterolobium ellipticum Benth. , Sclerolobium aureum (Tul.) Baill. and Vatairea macrocarpa (Benth.) Ducke, against Leishmania (Leishmania) amazonensis, yeasts and dermatophytes. Among the 26 extracts tested, more than 50% of the total demonstrated significant antifungal activity in comparison to the drug controls (minimal inhibitory concentration 0.12 to ≤31.25 g/ml). Six extracts capable of complete parasitic growth inhibition had the inhibitory concentration index for 50% values from 9.23 to 78.65 g/ml. The results led to the selection of the V. macrocarpa ethyl acetate root bark extract for chemical fractionation. This plant, traditionally referred to as angelim-do-cerrado or maleiteira, is used to treat superficial mycoses in Amazonia. A eishmania ungi previously unreported pterocarpan vatacarpan together with the known compound musizin was isolated. Vatacarpan demonstrated a minimal inhibitory concentration value of 0.98 g/ml against Candida albicans ATCC 10231, and thus comparable or superior to fluconazole and amphotericin B. The results add to literature’s information the ability of pterocarpans to act as antimicrobial agents. ileira © 2015 Sociedade Bras
Improving Activity Recognition using Temporal Regions

Recognizing activities in videos is an important task in computer vision area. Automatizing such task can improve the way we monitor activities since it is not necessary to have a human to watch every video. However, the classification of activities in a video is a challenging task since we need to extract temporal features that represent each activity. In this work, we propose an approach to obtain temporal features from videos by dividing the sequence of frames of a video into regions. Frames from these regions are merged in order to identify the temporal aspect that classify activities in a video. Our approach yields better results when compared to a frame-by-frame classification.
jmbReview The Photoheterotrophic Growth of Bacteriochlorophyll Synthase-Deficient Mutant of Rhodobacter sphaeroides Is Restored by I 44 F Mutant Chlorophyll Synthase of Synechocystis sp . PCC 6803

As protoporphyrin IX (PPn) is synthesized in a photosynthetic organism, it is chelated either with magnesium or with iron to form magnesium protoporphyrin IX (Mg-PPn) or heme, respectively. In an oxygenic photosynthetic organism, MgPPn is metabolized to chlorophyllide (Chlide) a, whose ring D is esterified with the C20 moiety of geranylgeranyl pyrophosphate (GGPP) by chlorophyll synthase (ChlG) (Fig. 1) to yield geranylgeranylated chlorophyll a (Chl agg) [31]. Chlorophyll reductase (ChlP) sequentially reduces the double bonds at positions 6, 10, and 14 of the geranylgeranyl (GG) moiety to form phytylated Chl a (Chl ap or Chl a) [2, 6]. In anoxygenic photosynthetic organisms, Chlide a is further metabolized to bacteriochlorophyllide (Bchlide) a. Bacteriochlorophyll hydratase (BchF) hydrates the C3vinyl group of ring A of Chlide a to form 3-hydroxyethyl Chlide a. Then, Chlide a oxidoreductase (COR) reduces ring B to form 3-hydroxyethyl Bchlide a, whose C3-hydroxyethyl group is subsequently oxidized to an acetyl group by Bchlide a dehydrogenase (BchC) [5]. Alternatively, the reaction of COR may precede that of BchF. Recently, a broad substrate specificity of BchC of Chlorobaculum tepidum [18] was found, proposing a new additional sequence of reactions in the order of BchF, BchC, and COR to synthesize Bchlide a. Subsequently, the ring D of Bchlide a is esterified with the C20 moiety of GGPP by bacteriochlorophyll synthase (BchG) (Fig. 1) to yield geranylgeranylated bacteriochlorophyll a (Bchl agg) [1]. Bacteriochlorophyll reductase (BchP) sequentially reduces the double bonds at positions 6, 10, and 14 of the GG moiety to form phytylated Bchl a (Bchl ap or Bchl a) [3]. The biosynthesis of Bchl a has been regarded as a Received: January 12, 2016 Revised: February 3, 2016 Accepted: February 11, 2016
Wireless camera network with enhanced SIFT algorithm for human tracking mechanism

In order to deal with the wireless camera networks (WCNs), whose detecting power of conventional camera networks with elasticity, reconfigurability and with a simple deployment of wireless sensor networks (WSN) for efficiently addressing the significant responsibilities in the method of cluster based human (object) tracking, such as integrating the measurement, including or ruling out in the cluster and cluster head rotation. The WCN effectively uses division friendly representation and methods in which every node contributes to the estimation in each methodology without the requirement of any previous information of the remaining the nodes. These methods are integrated in two different schemas so that they can be deployed with the same mean time without considering the cluster size. Thus, the observation and practical evaluation shows that the proposed schemes and methodology drastically reduces the energy consumption and computational trouble in accordance to the existing methodology.
Prejudice and Price : Corruption and Distributive Politics

Political corruption is a critical impediment to the success of transitioning democracies. Persistent corruption jeopardizes economic growth and delays democratic developments. Yet, voters are unwilling to vote out corrupt parties consistently, even while rising in protests and expressing indignation at the pervasiveness of political corruption. In this article, I study electoral backlashes against corruption by examining the link between corruption perceptions among voters and distributive policies incumbent parties use to assuage voter demand for electoral accountability. I present a theory of “corruption compensation”: corrupt incumbents strategically target higher shares of government resources to regions where corruption perceptions are higher and voters can credibly threaten to withdraw their electoral support. Using original, subnational data from Albania, I show that high corruption perceptions reduce incumbent support among voters, but resource provisions mitigate this effect. The findings supplement the electorate-based theories of distributive policies and contribute to an emerging literature on the political economy of distributive politics.
Improving Next Location Prediction by Using Hybrid Predictors

Neural networks, Bayesian networks, Markov models, and state predictors are different methods to predict the next context in a ubiquitous environment. For all methods a lot of parameters must be set up which differ for each user. Therefore a complex configuration must be made before a prediction method can be used. A hybrid predictor can reduce the configuration overhead. Such a hybrid predictor uses different prediction methods or configurations in parallel yielding different prediction results. A selector selects a prediction result from the result set of the base predictors. We propose and evaluate three principal hybrid predictor approaches – the warm-up predictor, the majority predictor, and the confidence predictor – with several variants. The hybrid predictors reached a higher prediction accuracy than the average of the prediction accuracies of the separately used prediction methods.
A new canid Nurocyon chonokhariensis gen. et sp. nov. (Canini, Canidae, Mammalia) from the Pliocene of Mongolia

A new genus and species of Canidae – Nurocyon chonokhariensis – is described from the Lower Pliocene lacustrine deposits of the Khirgisnur Formation of the Chonokhariakh-1 locality (northwestern Mongolia, the Great Lakes Depression). A well-preserved skull of the new canid demonstrates an unusual combination of primitive and derived features that distinguish it from other Pliocene members of the tribe Canini (Eucyon, Nyctereutes and Canis). Unlike Canis and like Eucyon and Nyctereutes, it shows primitive characters, such as the fan-shaped supraoccipital shield and limited posterior extension of the frontal sinus. It differs from Canis and Eucyon in its advanced, nearly hypocarnivorous type of tooth pattern, which it shares with Nyctereutes. At the same time, the advanced similarity in the dorsal expansion of the frontal sinus and in the loss of the vulpine groove support its relationship with Canis but distinguish it from the other Canini. Thus, the cranial morphology of the studied form does not indicate a close affi nity with any known genus of Pliocene Eurasian Canini and supports a full genus-level separation of this Mongolian dog. “Canis” adoxus from the Pliocene of Europe is the only fossil species to share similarities with Nurocyon. Phylogenetically, Nurocyon chonokhariensis probably represents a separate lineage of Canini which emerged during the early radiation and diversifi cation of Eurasian canids at the beginning of the Pliocene.
Towards using transparency mechanisms to build better mental models

Non-expert users often do not have accurate mental models of robots, leading to failures during interactive task learning. We first observe that the information required to identify the cause of interaction failures, can be classified into commonly-defined, uncommonly-defined and hidden features. We then implemented two transparency mechanisms, question-answering and visual explanation capabilities, through which a non-expert user can access the robot’s internal reasoning. We conducted a user study (N=64) where we measured the ability of the user to identify the reason for 12 distinct interaction failures. We first characterize the impact of the identified features of interaction failures, and then test the effect of the two transparency mechanisms on the user’s performance. Results show that transparency mechanisms have the most influence on interaction failures with uncommonly-defined and hidden features. These results confirm that transparency mechanisms are effective in improving the user’s performance, leading to a higher accuracy in the user’s mental model of the robot.
CS 4860-2019 fa Computational Foundations of Mathematics

Much has been written about the foundations of mathematics, referencing Aristotle, Euclid, Frege, Whitehead, Russell, Brouwer, Church, among many others. Exploring the foundations is a central theme of mathematical logic. These course notes offer a pragmatic account of foundations based on the fact that a substantial amount of mathematics is concerned with computation, both numerical and synthetic (as in Euclidean geometry). This observation alone leads to a clear and simple reason for understanding the computational facts that have rendered mathematics so important for more than two thousand years of human history. Steadily increasing computing power is making the role of logic more important as we illustrate in this course. There has never been a time when implemented thought tools grounded in mathematics are so fundamentally expanding our capacity to explore mathematics more deeply. The 20th century advent of computers as proof assistants has re-centered mathematics, setting new goals in which computers using large digital libraries of formalized mathematics are accelerating scientific discovery, finding unexpected connections among results, and solving long standing open problems. In due course this collaboration between humans and thinking machines will transform one of the oldest academic subjects, logic, into one of the most innovative and intellectually impactfull. The nature of computer science and the mechanisms it implements have created instances of machine intelligence that operate significantly beyond what unaided humans can achieve. Science-fiction visions look less imaginative than modern instances of the computational reality now supplanting them. We propose a research agenda for the role of machine intelligence in mathematical discovery.
Improving Bracket Prediction for Single-Elimination Basketball Tournaments via Data Analytics

The NCAA men’s basketball tournament highlights data analytics to everyday people as they look for help building their brackets. A k-Nearest Neighbors method was developed to improve the estimate of a team’s true winning percentage and thus improve the prediction of individual tournament match’s outcome. Several strategies were then developed to extend the single-game outcome prediction to the whole-bracket selection. Using data from year 2002-2013, the parameters in the k-Nearest Neighbors method were fine tuned. For the 2014 tournament, our method correctly predicted 42 games, which outperforms direct application of Pythegorean expectation (tabulated on kenpom.com) to the Log5 estimate. Compared to human pools, our method is better than 95% of the brackets submitted to the ESPN Tournament Challenge and the predictions from all credible college basketball analysts.
The Challenges of Using Neural Machine Translation for Literature

In this paper, we adapt state-of-the-art neural machine translation (NMT) systems to literary content and use them to translate ﬁction stories from English to Russian and from German to English. We show that such adapted systems have richer vocabulary and lead to improved automatic evaluation metrics on literary prose as compared to general domain NMT systems, including Google’s online MT. We propose a new error classiﬁcation scheme for NMT output that is speciﬁcally tailored to literary translation and let a bilingual evaluator analyze translated excerpts from two ﬁction stories. The results show that up to 30% of machine-translated sentences have acceptable quality. We observe very few severe syntactic errors even on complex sentences, but the meaning errors for ambiguous words are still numerous. A separate classiﬁcation of consistency, pronoun resolution, and tone/register error types reveals a high potential of MT quality improvement by considering the context of previous sentences or even the whole story. A preliminary experiment aimed at reducing pronoun translation errors conﬁrms this potential.