Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation and Expression Analysis of a Defensin Gene from Strawberry (Fragaria×ananassa cv. Paros)

Plant defensins are the cysteine-rich peptides that are encoded by small multi-gene families in the plant kingdom. In this study, we designed primers based on conserved regions of defensin genes to clone and identify defensin genes in strawberry (Fragaria×ananassa cv. Paros) by reverse transcription PCR technique. Sequence analysis showed that the deduced amino acid had significant similarity to other plant defensins from NCBI database and designated FaDef1. The predicted strawberry defensin protein encodes a 54 aa protein of 6.18 kDa, pI 9.22 and eight conserved cysteine residues with desired space conservation with other amino acids. Semi quantitative expressions of FaDef1 were analyzed in root, stem, leaf, flower, and fruit in three strawberry cultivars, namely, Queenelisa, Camarosa, and Paros. The results showed that the FaDef1 expression patterns were similar in different tissues of the three cultivars. The higher amount of relative expression of FaDef1 was in fruit and there was no observable expression in the root. The expression of FaDef1 increased after wounding and salicylic acid treatment. The expression level was higher in developed fruits compared to that of immature fruits. In fruits infected with the Gray mold agent (Botrytis cinerea), the expression of FaDef1 showed significant increase by development of disease symptom. Taken together, these results suggest that FaDef1 is both responsive to biotic stress signal compounds and strawberry B. cinerea and may be used as a candidate gene for engineering plants against
Fe b 20 16 Kirillov structures up to homotopy ∗

We present the notion of higher Kirillov brackets on the sections of an even line bundle over a supermanifold. When the line bundle is trivial we shall speak of higher Jacobi brackets. These brackets are understood furnishing the module of sections with an L∞-algebra, which we refer to as a homotopy Kirillov algebra. We are then led to higher Kirillov algebroids as higher generalisations of Jacobi algebroids. Furthermore, we show how to associate a higher Kirillov algebroid and a homotopy BV-algebra with every higher Kirillov manifold. In short, we construct homotopy versions of some of the well-known theorems related to Kirillov’s local Lie algebras. MSC (2010):16E45, 53D17, 58A50, 58E40.
Framing international media in the face of social movements: CNN and Al-Jazeera English in the fall of Morsi

This research paper aims to analyse and compare the role of two of the major television networks in international news coverage, Al-Jazeera English and CNN, in relation to one of the most important social movements of the last decade known as the Arab Spring. The study specifically focuses on Egypt during the coup of July 2013, when President-elect Mohamed Morsi was overthrown by the army after weeks of protests. Through a qualitative content analysis based on the framing theory, we will look at the position of both channels regarding a coup emerging from popular uprisings and identify the differences between the two channels. The results show frames matching the definition of conflict (crisis and coup), military power and the legitimacy of democracy. As for divergences, it highlights CNN’s stance towards the coup as a necessary evil, while Al-Jazeera English takes a more Islamist approach and prefers to emphasize its democratic legitimacy. The transition to democracy in Egypt is not seen as a linear process of uninterrupted progress, but events such as the coup are seen as slight setbacks in the development of the conflict. These setbacks are a result of the dynamics of power in Egypt, where the military remain in charge above and beyond social movements.
Dairy herd-level prevalence of Johne's disease and BVD in the intermountain west of the U.S.A. and farm management practices and characteristics for test-positive herds.

Herd-level prevalence of Mycobacterium avium subsp. paratuberculosis (MAP), causative agent of Johne’s disease (JD) and Bovine Viral Diarrhea (BVD) virus were estimated on dairy farms in Utah. Duplicate milks were collected at 3-4 day intervals on 5 dates from each bulk tank on participating farms. Samples were tested at separate laboratories for BVD (real-time, RT-PCR) and for JD/MAP (ELISA and qPCR). 151/209 (72%) eligible dairy farms participated. Farms detected positive were: 58 JD (38%) and 14 BVD (9%); 5 farms had both diseases. Follow up visited farms’ (n=22) means, medians: 778,420 milking cows; 20,052 lbs, 20,311 lbs 305d milk; 175,545/ml, 178,000/ml bulk milk SCC; stalls visibly soiled in rear one-third 37%, 32%, range 5% to 90%. Seventeen of 21 (81%) farms with JD had observed adult cows becoming thin while retaining appetite, 52% had seen adult cows contract diarrhea and subsequently die. Both BVD-positive farms had observed abortions. Free stalls housed milking cows on 91% of farms; dry lots housed dry cows on 55%. Nine farms (41%) had purchased animals within the past year: 27% pregnant heifers, 18% bulls, 9% calves, 14% cows; 9% had purchased only bulls. Whenever animals were last purchased, 14 farms (64%) had performed no disease testing or segregation; 8 farms (36%) utilized at least one biosecurity practice for replacements. Most common were 9-way vaccine including BVD on arrival (27%), 14% segregated replacements for any time, 11% tested for any diseases (none for JD). Fourteen (67%) farms with JD would identify known positive cows; none would segregate positives. Most producers (57%) allowed known JDpositive cows to calve again, farms with BVD were equivocal. No producers would have a separate calving area for JD or BVD-positive cows. Six farms (27%) fed calves only individual cow colostrum and pasteurized milk. All 22 farms vaccinated against BVD.
USING MULTI-ANGLES EVOLUTIONARY ALGORITHMS FOR TRAINING TSK-TYPE NEURO-FUZZY NETWORKS

The development of a global-based method for building robust neuro-fuzzy networks has become an interesting issue. Among the various building methods, the evo- lutionary algorithms provide robust ways increasing the chances of meeting the optimal solution. However, evolutionary algorithms may only use a single angle to evaluate the searching space to obtain the optimal solutions. It implies that they may slowly or even hardly meet the optimal solution. Thus, the current study provides a novel architec- ture that uses multiple angles for evaluating the searching space. More specically, the novel architecture adopts multiple angles to improve the evolutionary process by dynam- ically adjusting the searching space. By doing so, the proposed architecture can increase the chances of meeting the optimal solution. As shown in the results, the proposed ar- chitecture outperforms other existing evolutionary algorithms. Based on the results, a framework is proposed to build a benchmark for developing evolutionary algorithms that consider the multiple angles of the solution space.
The Hull and White Model of the Short Rate: An Alternative Analytical Representation

Hull and White extend Ho and Lee's no-arbitrage model of the short interest rate to include mean reversion. This addition eliminates the problem of negative interest rates and has found wide application. To implement their model, Hull and White employ a sequential search process to identify the mean interest rate in a trinomial lattice at each date. In this paper we extend Hull and White's work by developing an analytical solution for the mean interest rate at each date. This solution applies equally well to trinomial lattices, interest rate trees, and Monte Carlo simulation. We illustrate the analytical result by applying it to an example originally used by Hull and White and then for valuing an option on a bond.
Determining optimal volume fractions of a municipal wastewater treatment plant by dynamic simulation

The aim of this paper is to present the results of an optimisation process of a municipal wastewater treatment plant. The plant was designed to be capable of biological excess phosphorous removal though effluent phosphorous concentrations were above the limit value for several occasions. In the summer period the total nitrogen concentration was also higher than required. It was examined how the adjustment of volume fractions contribute to effluent quality and the optimal ratios were determined by use of dynamic simulation techniques. The aerobic volume fraction was not altered only the two un-aerated phases were modified. The results showed that the anaerobic volume fraction could be lowered up to 11.5 % concluding the anoxic ratio to be 34.5 %.
Organizational Profitable by Quality Improvement

Recognizing the importance of quality improvement system in achieving flexibility in an international context expands the types of research questions related to the role of quality improvement system functions in organizational performance, such as selection of quality improvement resources, training, and compensation and performance appraisal. The form and structure of an organization's quality improvement system can affect organizational profitable motivation levels in several ways. Organizations can adopt various quality improvement practices to enhance organizational profitable. This paper reviews the available information about important service economy sector criteria such as world trade, organizational profitable, quality improvement, quality improvement system, employment, productivity, quality, etc. in an international scale. The end of this paper is able to find out how important the organizational profitable is, comparing with other economy sectors and how fast the growth of the mentioned sector is. Furthermore, the position of quality and productivity in the service sector will be specified and the use of quality improvement techniques for improving service quality will become necessary.
Rao-Blackwellized Particle Gibbs Kernels for Smoothing in Jump Markov Nonlinear Models

Jump Markov nonlinear models (JMNMs) characterize a dynamical system by a finite number of presumably nonlinear and possibly non-Gaussian state-space configurations that switch according to a discrete-valued hidden Markov process. In this context, the smoothing problem– the task of estimating fixed points or sequences of hidden variables given all available data-is of key relevance to many objectives of statistical inference, including the estimation of static parameters. The present paper proposes a particle Gibbs with ancestor sampling (PGAS)-based smoother for JMNMs. The design methodology relies on integrating out the discrete process in order to increase the efficiency through Rao-Blackwellization. The experimental evaluation illustrates that the proposed method achieves higher estimation accuracy in less computational time compared to the original PGAS procedure.
Effects of Ultraviolet Radiation on Locomotion and Orientation in Roughskin Newts (Taricha granulosa)

Environmental changes, including those associated with the atmosphere may significantly affect individual animals and ultimately populations. Ultraviolet (UV) radiation, perhaps increasing due to stratospheric ozone depletion, has been linked to mortality in a number of organisms, including amphibians. The eggs and larvae of certain amphibian species hatch at significantly lower rates when exposed to ambient ultraviolet light. Yet little is known about the sublethal effects of UV radiation. For example, UV radiation may affect specific behaviors of an animal that could alter its ability to survive. To examine if UV radiation affects amphibian behavior, we used roughskin newts (Taricha granulosa) as a model. Newts were exposed to low-level doses of UV in the laboratory and then tested in the field to examine if UV-exposed and control (no UV) newts differed in orientation towards water or in locomotor activity levels. UV-exposed and control newts both exhibited a significant orientation towards water in field tests but there was no significant difference in orientation between treatments. However, UV-exposed newts were significantly more active than control newts. Our results suggest that exposure to short-term low levels of UV radiation alters certain behaviors. Environmentally induced changes in behavior may have significant ecological and evolutionary consequences.
Housing Prices, Externalities, and Regulation in U.S. Metropolitan Areas

Housing prices vary widely between U.S. markets. The purpose of this study is to analyze determinants of housing prices, with particular focus on the effects of regulations in land and housing markets. The basic unit of observation is the city or metropolitan area. The innovative part of the data collection is constructing indexes that reflect regulatory regimes in different markets. The basic method is to model house prices and rents in a simple supply and demand framework focusing on incomes, population changes, "noneconomic" determinants (such as topographical features), and other supply conditions, notably measures of the regulatory environment.
Distinguishing long non-coding RNAs from protein coding transcripts based on machine learning techniques

Among the analyses that have to be performed in sequencing projects, an important problem to be addressed is the distinction of protein coding transcripts (PCTs) and long non-coding RNAs (lncRNA). This work investigates potential characteristics of the lncRNAs and proposes two methods for distinguishing these two classes of transcripts (PCTs and lncRNAs). The first methods was based on Support Vector Machine (SVM), while the second one used semi-supervised learning techniques. The SVM based method obtained excellent results when compared to other methods in the literature. This method was trained and tested with data from human, mouse and zebrafish, and reached accuracy of ≈ 98% for human and mouse data, and ≈ 96% for zebrafish data. Besides, models with multiple species were created, which improved the classification for species different from those used in the training phase, i.e., these models could also be used in the classification of species different from those that were used in the training phase. To validate this method, data from rat, pig and drosophila, and RNA-seq data from humans, gorillas and macaque were used. This validation reached an accuracy of more than 85% for all the species. Finally, this method was able to identify two sequences within the Swiss-Prot database that were reannotated. The semi-supervised based method was trained and tested with data from human, mouse, platypus, chicken, opossum, orangutan and xenopus, in five semi-supervised learning techniques. The contribution of this method was the reduction of the size of the classified training data set. In the best scenario, only two annotated sequences were used in the training phase, which is an expressive gain when compared to other tools available in the literature. Accuracies obtained by the method in the best cases were ≈ 95% for human and mouse datasets, ≈ 90% for chicken, opossum and orangutan datasets, and ≈ 80% for data platypus and xenopus datasets. RNA-seq data were used for testing, having obtained more than 95% of accuracy. This data was used to train the orangutan and xenopus models, also leading to an excellent accuracy.
Dynamic bit rate assignment in wireless CDMA data transmissions

This paper compares different schemes that can be used to improve the performance of CDMA in wireless data systems. In particular, we consider a closed-loop power control scheme and an adaptive coding scheme. The comparison between these different techniques is performed in terms of mean transmission time and energy efficiency. Our aim is to show that an adaptive coding scheme can allow good performance with respect to typical power control techniques.
A comparative evaluation of models of lactating ruminant

Nutrition of lactating ruminant has been the aim of several mathematical models for several decades. A comparative study of these models was performed on a limited number of aspects. At the digestive level certain models were based on compartmental structure while others were built on stretched flows ones. Only few systematic comparisons of the models of digestion have already been carried out and it appears necessary to achieve a more detailed level of comparison. On some important aspects, the level of knowledge, and therefore the reliability of digestive models, remains largely unsufficient. This is particularly the case of the production and absorption rates of volatile fatty acids in the reticulo rumen. Quantitative knowledge remains also unsufficient on the absorbed flows of glucose and long chain fatty acids in the small intestine. On metabolic aspects the already published or used models are also largely different one from each others. The level of aggregation, which can be roughly evaluated with the number of tissular and metabolic compartments differs largely between models. This number ranges from LT 10 to more than 50 for research models. Another important feature on which models largely differ concerns the regulating system. It was in all models more or less theoritical due to the lack of knowledge to elaborate a mechanistic model of regulations. Otherwise the published regulating systems took into consideration at various degree the homeostatic and homeorhetic drives. The comparison of the existing models of lactating ruminant was also reviewed through the major organs (splanchic area, liver, protein, lipids, udder) and metabolic areas (metabolism of nitrogen, lipids and minerals).
Exploiting Category-Specific Information for Image Popularity Prediction in Social Media

Social media has become an important part of each individual’s life and an invaluable tool for companies to know their customers better in the market. The problem of popularity prediction in social media has been studied extensively over the past few years. Yet, it is still a challenging task due to various factors, including the difficulty to measure the preference of viewers towards specific post contents, the influence of user popularity, and the properties of social media itself. Accordingly, this paper focuses on image popularity prediction by analyzing early popularity patterns of posts in the same content category, fused with user information data. We conduct extensive experiments on a dataset crawled from Instagram. The experimental results show that our proposed model achieves considerable performance in predicting the number of likes of an Instagram input photo. Moreover, we also provide in-depth analysis of the importance of category-specific information in popularity prediction.
Coconut Shell Activated Carbon as an Alternative Renewable Energy

Research coconut shell activated carbon as an alternative renewable energy has been done. Coconut shell activated carbon is processed into fuel that can replace coal. Coconut shell densely textured, hard, and old became a mainstay to be used as activated carbon. The process of forming into a coconut shell coconut shell activated carbon made by drying, pyrolysis, and carbonization. After it was examined DSC and SEM, so the coconut shell activated carbon obtained temperature of 500 0C is activated carbon which can produce the greatest heat and durable because of the many elements contained carbon and many pores are formed. Keywords : activated carbon; alternative renewable energy; coconut shell.
Resale price maintenance: Explaining the controversy, and small steps towards a more nuanced policy

The paper sets out why we consider that the legal framework in the EU amplifies what are in reality relatively small differences in thinking around RPM. Primarily, this is because it asks economists, in the name of legal certainty, to draw a false dichotomy between agreements and practices which are harmful and those which are beneficial. We then provide a summary of the literature on RPM and, based on this thinking, set out a few small steps that might be taken towards a more nuanced approach to assessing RPM, within a 'presumed illegality' framework without sacrificing the beneficial legal certainty that the current approach brings.
Automatic Training Sample Selection for a Multi-Evidence Based Crop Classification Approach

Abstract. An approach to use the available agricultural parcel information to automatically select training samples for crop classification is investigated. Previous research addressed the multi-evidence crop classification approach using an ensemble classifier. This first produced confidence measures using three Multi-Layer Perceptron (MLP) neural networks trained separately with spectral, texture and vegetation indices; classification labels were then assigned based on Endorsement Theory. The present study proposes an approach to feed this ensemble classifier with automatically selected training samples. The available vector data representing crop boundaries with corresponding crop codes are used as a source for training samples. These vector data are created by farmers to support subsidy claims and are, therefore, prone to errors such as mislabeling of crop codes and boundary digitization errors. The proposed approach is named as ECRA (Ensemble based Cluster Refinement Approach). ECRA first automatically removes mislabeled samples and then selects the refined training samples in an iterative training-reclassification scheme. Mislabel removal is based on the expectation that mislabels in each class will be far from cluster centroid. However, this must be a soft constraint, especially when working with a hypothesis space that does not contain a good approximation of the targets classes. Difficulty in finding a good approximation often exists either due to less informative data or a large hypothesis space. Thus this approach uses the spectral, texture and indices domains in an ensemble framework to iteratively remove the mislabeled pixels from the crop clusters declared by the farmers. Once the clusters are refined, the selected border samples are used for final learning and the unknown samples are classified using the multi-evidence approach. The study is implemented with WorldView-2 multispectral imagery acquired for a study area containing 10 crop classes. The proposed approach is compared with the multi-evidence approach based on training samples selected randomly and border samples based on initial cluster centroids within agricultural parcels without any refinement. The results clarify the improvement in overall classification accuracy to 82.3 % based on the proposed approach from 74.9 % based on random selection and 71.4% on non-refined border samples.
Value Sharing and Food System Dynamics for Milk , Tomato , and Cereals Food Chains

The aim of the paper is to analyse value sharing and food system dynamics of milk, tomato, and cereals food chains, so to explore the agro‐food enterprises capacity to be competitive and sustainable. The paper is based on the functionalist approach of Malassis and the notion of the system of the General Systems Theory. The methodology is aimed at creating a consolidated financial statement for each food chain so to re‐create the chain value and identify how this is shared among the different food chain stages. The analysis is carried out on primary and secondary data: around 2400 financial statements concerning 480 enterprises from 2003 to 2007 and stakeholders’ interviews. Results show that value is usually created in the processing and distribution stages, to the detriment of the primary sector, and that the retail managing practices tend to impose damaging structural changes on farms whose profitability is at times becoming sustainable only thanks to European subsidies. To conclude, there is evidence of inadequate definition of strategic and network alliance along the chain. Competitiveness is still a concept achieved by single food chain stages against others and food chain internal competition entails a declining sustainability of small farms and enterprises.