Miracle-dz/UniFAR-FTUs huggingface dataset:
The methylation pattern of DNA and complex correlations with gene expressions during TuMV infection in Chinese cabbage

Chinese cabbage (Brassica rapa L. ssp. pekinensis) is one of the most important economic crops. However, its yield and quality can be severely threatened by Turnip mosaic virus (TuMV). Emerging evidence indicates that epigenetic mechanisms, especially DNA methylation, play an important role in regulating gene expression. Therefore, identification of resistance genes modified by DNA methylation during the virus infection would provide a critical clue for improving disease resistance breeding programs. Here, we present detailed analysis for the correlation of DNA methylation and gene expression involved in several anti-pathogen pathways. We also found that different methylation patterns exist in different methylation sites (CG, CHG, and CHH, where H represents A, G, or T) and genomic regions. Furthermore, we identified disease-resistant genes related to the nucleotide binding site-leucine-rich repeats family, auxin, salicylic acid signaling transduction, cell wall biosynthesis, and protein degradation among the different methylated genes (DMGs) suggesting that these genes may be modified by DNA methylation and work together to activate an immune response. The identified DMGs are a valuable resource for discovering resistance genes. Our study not only provides valuable data for future biotechnology research and epigenetic studies, but also helps to explore how the epigenetic mechanisms modify antiviral pathways.
Structural Decomposition Analysis of Global Metal Footprint Change, 1995–2013

With the increasing competition for limited metal ore resources, the impact of capital formation on nations’ metal footprints have been studied broadly. However, the driving forces and pathways of metal footprint change still haven’t been fully understood. Therefore, in the present study, the additive structural decomposition analysis is adopted to decompose the world metal footprint from 1995 to 2013 into five major driving factors. The obtained results indicate that: i) the modern production structure has been becoming increasingly dependent upon metal resources; ii)the production structure effect and consumption volume effect are dominant factors driving the metal footprint increase, yet the intensity effect is the main force offsetting this inclining trend; iii) all the driving factors in developing countries tend to be more divergent comparing with developed countries consistent with their economic growth characteristics. The findings of this study are significant for policy makers to understand the deep causes for domestic metal footprint change and propose proper relevant polices.
Antibiotic resistance gene profiles at various treatment stages of a full-scale municipal sewage plant

aLaboratory of Wastewater Management and Treatment Technologies, Department of Environmental Engineering, Democritus University of Thrace, Vas. Sofias 12, 67132 Xanthi, Greece, Tel. /Fax: +302541079313; emails: sntougia@env.duth. gr (S. Ntougias), izerva@env.duth. gr (I. Zerva), pmelidis@env.duth. gr (P. Melidis) bMicrobiology Laboratory, Medical School, Democritus University of Thrace, Campus (Dragana), 68100 Alexandroupolis, Greece, emails: ialexand@med.duth. gr (I. Alexandropoulou), mpanopou@med.duth. gr (M. Panopoulou)
Characterization of two sites for geotechnical testing in permafrost: Longyearbyen, Svalbard

The mean annual air temperature in Svalbard has increased between 3 ℃ and 5 ℃ during the last 40 to 50 years. The continuous warming trend observed in Svalbard during the last 30 years has raised concerns about the stability and durability of existing infrastructure on permafrost and uncertainties related to the design of new structures and infrastructure in the region. It is therefore of special interest and importance to establish a reference field site for geotechnical testing in permafrost in Svalbard. Two benchmark sites are established near Longyearbyen, Svalbard (78°13'N, 15°28'E) for geotechnical testing and evaluation of field investigation methods in saline, permafrost soils. These sites, named “Adventdalen” and “UNIS East” based on their locations, form part of the research infrastructure of the Norwegian GeoTest Sites project. Since 2016, efforts have focused on geotechnical and geothermal characterization and instrumentation of the upper 30 m of the soil stratigraphy. Field investigations included drilling and core retrieval, installation of thermistor strings, CPTU, and ERT. Laboratory investigations focused on index testing and the evaluation of soil thermal properties. This paper characterizes soil conditions at these sites and may serve as a reference for others working with saline, permafrost soils.
A Modified Spline Interpolation Method for Function Reconstruction from Its Zero-Crossings

There are different algorithms for reconstruction of a one-dimensional function from its zero-crossings. However, none of them is stable and computable in real time. Methods for one-dimensional function reconstruction from its generalized zero-crossings based on cubic spline interpolation are introduced in this paper. The main goal is to build an algorithm which is able to reconstruct a smooth function from the generalized zero-crossings as close to the original function as possible. The three main advantages of the approaches described in this paper are reliability, stability, and linear time processing.
Extraction Opinion of Social Media in Higher Education Using Sentiment Analysis

The purpose of this research is to extract social media Twitter opinion on a tertiary institution using sentiment analysis. The results of sentiment analysis will provide input to universities as a form of evaluation of management performance in managing institutions. Sentiment analysis generated using the Naïve Bayes Classifier method which is classified into 4 classes: positive, normal, negative and unknown. This study uses 1000 data tweets used for training data needs. The data is classified manually to determine the sentiment of the tweet. Then 20 tweet data is used for testing. The results of this study produce a system that can classify sentiments automatically with 75% test results for sentiment, some obstacles in processing real-time tweets such as duplicate tweets (spam tweets), Indonesian structures that are quite complex and diverse.
Covering 3-coloured random graphs with monochromatic trees

We investigate the problem of determining how many monochromatic trees are necessary to cover the vertices of an edge-coloured random graph. More precisely, we show that for p ( lnn n )1/6 in any 3-colouring of the random graph G(n, p) we can find 3 monochromatic trees such that their union covers all vertices. This improves, for three colours, a result of Bucić, Korándi and Sudakov [Covering random graphs by monochromatic trees and Helly-type results for hypergraphs, arXiv:1902.05055]
An efficient and Automatic Algorithm for Image Forgery Detection

With the development of sophisticated image editing and manipulation tools, the originality and authenticity of a digital image is usually hard to determine visually. In order to detect digital image forgeries, various kinds of digital image forensics techniques have been proposed in the last decade. Compared with active forensics approaches that require embedding additional information, passive forensics approaches are more popular due to their wider application scenario, and have attracted increasing academic and industrial research interests. A novel copy-move forgery detection scheme using adaptive over-segmentation and feature point matching is proposed in this paper. The proposed scheme integrates both block-based and key point-based forgery detection methods. First, the proposed Adaptive OverSegmentation algorithm segments the host image into non-overlapping and irregular blocks adaptively. Then, the feature points are extracted from each block as block features, and the block features are matched with one another to locate the labelled feature points; this procedure can approximately indicate the suspected forgery regions. To detect the forgery regions more accurately, we proposed, the Forgery Region Extraction algorithm, which replaces the feature points with small super pixels as feature blocks and then merges the neighbouring blocks that have similar local colour features into the feature blocks to generate the merged regions; finally, it applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the proposed copy-move forgery detection scheme can achieve much better detection results even under various challenging conditions compared with the existing state-of-the-art copy-move forgery detection
Cognitive architecture of a humanoid robot for coaching physical exercises in kinaesthetic rehabilitation

The rising number of the elderly incurs growing concern about healthcare, and in particular rehabilitation healthcare. Assistive technology and assistive robotics in particular may help to improve this process. We develop a robot coach capable of demonstrating rehabilitation exercises to patients, watch a patient carry out the exercises and give him feedback so as to improve his performance and encourage him. We propose a general software architecture for our robot coach, which is based on imitation learning techniques using Gaussian Mixture Models. Our system is thus easily programmable by medical experts without specific robotics knowledge, as well as capable of personalised audio feedback to patients indicating useful information to improve on their physical rehabilitation exercise. This article demonstrates that such our cognitive architecture is suitable for the targeted application and tests that probabilistic approaches of imitation learning based on Gaussian Mixture Models can detect errors in the execution of a dynamic movement. I. ROBOT COACH FOR PHYSICAL REHABILITATION
A social network analysis approach to a digital interactive storytelling in mathematics

In this paper we present a social analysis of the interactions among the students involved in a trial of the Italian PRIN project “Digital Interactive Storytelling in Mathematics: a Competence-based Social Approach”. The instructional design is based on collaborative scripts within a digital storytelling framework where the story follows the interactions among the characters played by the students and an expert (teacher or researcher). We report the results of a trial that involved teachers and students from the upper secondary school, analysing from a Social Network Analysis point of view the interventions of the expert, the involvement/participation of the students and the interactions among peers and with the expert. We also briefly discuss potentialities and limitations of the currently available tools to perform this kind of analysis, in view of the broader perspective offered by the Learning Analytics approach
Rapid and environmentally friendly determination of trace nitrite in water under combined effects of cetyltrimethyl ammonium bromide and β-cyclodextrin

Nitrite is a characteristic pollutant in natural water. A simple, rapid and highly selective spectrofluorimetric method for determination of trace nitrite has been developed. It was based on the fact that nitrite reacted rapidly with iodide at room temperature and led to the fluorescence quenching of 4′,5′-dibromofluorescein (DBF) under the combined effects of cetyltrimethyl ammonium bromide (CTAB) and β-cyclodextrin (β-CD). The fluorescence intensity of the above reaction system was measured with excitation and emission wavelengths of 532 and 551 nm, respectively. Optimal values of the main factors involving volumes of CTAB, β-CD and sulfuric acid were explored by a Box–Behnken design. Under the optimized experimental conditions, the fluorescence quenching intensity was good linear over a nitrite concentration range of 0.2–46.0 μg/L with a correlation coefficient better than 0.998. The detection limit of 0.16 μg/L was obtained for the determination of nitrite by the proposed method. The general coexisting ions did not interfere to the reactions of nitrite with iodide and DBF. The relative standard deviation of the method for the determination of nitrite in water samples was below 2.7%, and the corresponding recoveries were between 92.1% and 107.9%. The proposed method is environmentally friendly and suitable for water monitoring.
Are story outlines confined within seven basic plots

This research will discuss on a unique method to model story plot data by analyzing the pattern of events in the text content and then clustering the story plots into seven categories that would imply a story outline. Processing the story outlines and categorizing stories based on pattern of events in stories will aid in improving recommender systems. The idea behind this type of a model is to cluster data based on context from the text data rather than just topic words present in the data. In this research a FrameNet classifier is modeled to classify the sentences into frames. the frame classifier model was built using a Linear Kernal SVM. The model showed a 67% accuracy predicting the frames. The sequence of frames are transformed into numeric representations and then clustered using k means clustering algorithm to fit within seven clusters. The model was evaluated using an elbow test and silhouette analysis to verify the number of clusters chosen which showed a good result of 6 as optimal number of clusters.
Literacy: From the Perspective of Text and Discourse Theory

Literacy is a critically important and contemporary issue for educators, scientists, and politicians. Efforts to overcome the challenges associated with illiteracy, and the subsequent development of literate societies, are closely related to those of poverty reduction and sustainable human development. In this paper, the authors examine literacy from the lens of text and discourse theorists who focus on the higher-order comprehension processes involved in literacy. Discourse processing models make the assumption that comprehension emerges from the construction of a mental model of the text, which relies on the reader generating inferences to connect ideas within the text and to what the reader already knows. The article provides a broad overview of the theoretical models that drive research on text comprehension and production, as well as how this research shapes literacy instruction and effective interventions. The authors focus on two interventions with proven success in improving deep comprehension and writing, iSTART and the Writing Pal. Increasing literacy across the world call for a greater focus on theory driven strategy interventions to be integrated within classrooms and community at large.
Inclusion Complexation in Sulfobutyl Ether Beta Cyclodextrin and Dispersion in Gelucire for Sustained Release of Nifedipine Employing Almond Gum

In the present research investigation, the utility of employing high dissolving forms of nifedipine for sustained release from matrix tablets with almond gum as major release retardant is explored. A poorly soluble BCS class II drug nifedipine is chosen as a model drug. Efforts were made to alter the dissolution characteristics of the drug before it is entrapped in the polymer matrix of almond gum. Inclusion complexation in sulfobutyl ether beta cyclodextrin or solid dispersion in gelucire (50/13) resulted in enhanced dissolution of nifedipine. The high dissolving forms are characterized by x-ray diffraction, differential scanning calorimetry and infra-red spectroscopy. The matrix tablets prepared employing the high dissolving forms exhibited satisfactory characteristics such as hardness, friability, swelling index. The drug release is found to be slow and spread over a period of 12 hours and the release could be modified with changes in nature of high dissolving form and the proportion of almond gum as the major release retardant. Employing high dissolving forms in matrix tablets of almond gum is found to be a novel approach in obtaining slow and complete release of poorly soluble drug such as nifedipine.
Analysis of Speech Separation Methods based on Deep Learning

In this paper, we perform an analysis of speech separation methods using deep learning. We provide a review of the literature, discussing the main features of the works based on audio and audiovisual processing, as well as the deep learning method. For the analysis of the articles, we provide a description where we identify the category, characteristics, methodology, results, and application. According to the study, we observed that the acoustic-based algorithms require audio portions of voices with external interferences to improve the intelligibility and the quality of the voice signals. Audio-visual-based methods use thousands of hours of segments of noisy videos to obtain stable performances and enhancing the quality of the separation. This latter category, also known as the cocktail party problem represents an ongoing open problem in the deep learning community.
RELATIONSHIPS BETWEEN MACROINVERTEBRATE COMMUNITIES AND ENVIRONMENTAL PARAMETERS OF THE HU-LAN ESTUARY NATURAL WETLAND RESERVE AND ITS SURROUNDING WATERS, NORTHEAST CHINA

This study analyzed the relationships between environmental parameters and benthic macroinvertebrate faunas. Invertebrate richness, Shannon-Wiener diversity, functional-group and CCA were used to evaluate conditions of waterbodies of the Hu-lan Estuary Natural Wetland Reserve and its hinterlands. We measured 10 physicochemical variables and collected 165 species belonging to 6 functional feeding groups (FFGs) during spring, summer and autumn. Some of the physicochemical factors changed with seasons. Nitrate nitrogen, total nitrogen and total phosphate significantly varied with seasons (p ˂ 0.05), unlike water temperature, depth, pH, dissolved oxygen, chemical oxygen demand, Ammonia nitrogen and electronic conductivity (p > 0.05). The taxa richness of benthic macroinvertebrates did not vary significantly within the sites (p = 0.508); while the Shannon-Wiener diversity showed significantly difference (p ˂ 0.01) within the sites over these three seasons. Gatheringcollectors were the most abundant macroinvertebrate FFG assemblages all over the sites, though there’s no significant difference in the total abundance of 6 functional feeding groups identified among the sites during spring, summer and autumn (p = 0.677, p = 0.681, p = 0.302, respectively). Our CCA results indicated positive correlations of macroinvertebrate community compositions with environmental variables in the Hu-lan Estuary Natural Wetland Reserve and its surrounding waters.
Intrusion Detection System Using Machine Learning Approaches

Network security is becoming an important issue in the field of information security. Hackers and Intruders can make many successful attempts to break down into networks or computer systems, and so overcome the need to create a powerful Intrusion Detection System (IDS) is a primary need. IDS is the art of detecting attacks and any attempt to break down networks, also it‟s an effective tool to prevent unauthorized access to any network by analyzing its traffic. The aim of this research is to build an Intrusion Detection Framework able to classify network activities, „Normal‟ or „Attack‟, using different Machine Learning algorithms, Random Forest (RF), Multi-Layer Perceptron (MLP), and Library for Support Vector Machine (LIBSVM). The proposed model had been tested by using a common dataset called NSL-KDD. This paper investigates two techniques, the first technique is to apply the different Machine Learning algorithms over the NSL-KDD dataset, and the second technique used a Feature Selection algorithm called Correlation Feature Selection „CFS‟, to drop some irrelevant attributes in the dataset, to cut the time taken in the training and testing phases, Random Forest shows a superior response compared to the other algorithms, especially in terms of response time, detection rate and false positive rate.
Association Of Hpv And Oral Cancer

According to the International Research on Cancer (AIRC) oral cancers are a type of cancer that develops in people’s lip linings, mouth, and the oropharynx. Studies have shown that the consumption of alcohol and tobacco products are the primary carcinogens that cause oral cancers. However, increasing cases of young individuals with no previous exposure to alcohol or tobacco who are getting oral cancers have prompted studies to look into the role of other carcinogens, especially viruses. Currently, the IARC presents oral cancers as the sixth most common type of cancer in the world. High-risk human papilloma virus (HPV) types are responsible for over 50 percent of cervical cancers worldwide. Also, people also acquire HPV in the mouth, prompting studies to look into the virus’s association with oral cancers. This paper performs a systematic review of studies investigating the association of oral cancers and HPV. The association of HPV and oral cancers began in the 1980s; however, more studies performed in the last decade until now are showing a positive association. From this research, it becomes clear that knowledge about oral HPV and oral cancers is expanding. Emerging research shows that the DNA of the HPV-16 virus are found on oral cancer tumors; hence, a positive causal association. More research is also coming with significant evidence to suggest a causal relationship between HPV and some oral cancers, most notably, those of the tongue and tonsils among people with no previous exposure to the usual carcinogens (Alcohol and tobacco).
Spoken Arabic Dialect Identification Using Motif Discovery

In traditional Dialect Identification (DID) approaches, regardless of the level and type of features used for identification,they use either predefined references such as phones, phonemes, or even acoustic sounds that characterize a language/dialect, or involve some sort of transcription of the input data. The transcription may be manual or automatic using tools such as ASRs,Tokenizers, or Phone Recognizers. In this paper, we introduce a new approach based on analyzing the speech signal directly and extracting the features that characterize the dialect without any predefined references and without any sort of transcription. The main idea is that we find the repeated sequences (motifs) of the dialect by treating the speech signal as a times series, so we can apply motif discovery techniques to extract the repeated sequences directly from the speech signal. For motif extraction, weadopted an extremely fast parameter-free Self-Join motif discovery algorithm called Scalable Time series Ordered-search Matrix Profile (STOMP). We implemented the new approach in two stages; in the first we built a base line system in which we extracted 12 Mel Frequency Cepstral Coefficients (MFCC) from each motif, in the second stage we built an improved system using 39 coefficients by adding 13 Delta coefficients, 13 Delta-Delta coefficients, and 1 Log Energy coefficient. In both systems, we used Gaussian Mixture Model-Universal Background Model (GMM-UBM) as a classifier. We applied our new approach on threedifferent motif lengths 500ms, 1000ms, and 1500ms using 1gmm component up to 2048gmm components. We downloaded the data set from Qatar-Computing-Research- Institute domain. We carried out our experiments on different Arabic dialects: the Egyptian (EGY), Gulf (GLF), Levantine (LEV), and North African (NOR). The base line results were very competitive with the traditional, more sophisticated approaches, while the improved system showed very good result. The improvement was so significant that we can consider the new approach as competitive, simple, and dialect-independent approach.