Miracle-dz/UniFAR-FTUs huggingface dataset:
Flavonoids, antioxidant potential and antimicrobial activity of Myrcia rufipila mcvaugh leaves (myrtaceae)

Abstract This paper reports the first chemical study of the non-volatile compounds, antioxidant capacity and antimicrobial effect of the methanol extract of the leaves of Myrcia rufipila McVaugh. Samples of the leaves were collected in Maracanã Municipality, Pará, Brazil. The chemical investigation led to the identification of the triterpenoids β- and α-amyrin, the flavonoids 4'-O-galloyldihydromyricetin, myricetin, myricitrin, desmantin-I, myricetin-3-O-(3"-O-galloyl)-α-L-rhamnopyranoside and isovitexin, in addition to gallic acid. The methanol extract showed antioxidant capacity (>90%) against DPPH radical (IC50 356.3 ± 3.1 μg.mL−1) and was active only at high concentrations against the tested microorganisms, including the chloramphenicol resistant E. coli CCMB261 and S. aureus CCMB285 and a nystatin resistant C. parapsilosis CCMB 288. This study shows that M. rufipila, like other Myrcia species, is another source of flavonoids such as desmantin-I and myricitrin which have shown hypoglycemic potential, besides triterpenes and phenolic acids. Graphical Abstract
The Naked Mole-Rat: An Unusual Organism with an Unexpected Latent Potential for Increased Intelligence?

Naked mole-rats are eusocial, hairless mammals that are uniquely adapted to their harsh, low-oxygen subsurface habitat. Although their encephalization quotient, a controversial marker of intelligence, is low, they exhibit many features considered tell-tale signs of highly intelligent species on our planet including longevity, plasticity, social cohesion and interaction, rudimentary language, sustainable farming abilities, and maintaining sanitary conditions in their self-built complex housing structures. It is difficult to envision how naked mole-rats would reach even higher levels of intelligence in their natural sensory-challenged habitat, but such an evolutionary path cannot be excluded if they would expand their range onto the earth’s surface.
The Embodied God: Core Intuitions About Person Physicality Coexist and Interfere With Acquired Christian Beliefs About God, the Holy Spirit, and Jesus

Why are disembodied extraordinary beings like gods and spirits prevalent in past and present theologies? Under the intuitive Cartesian dualism hypothesis, this is because it is natural to conceptualize of minds as separate from bodies; under the counterintuitiveness hypothesis, this is because beliefs in minds without bodies are unnatural-such beliefs violate core knowledge intuitions about person physicality and consequently have a social transmission advantage. We report on a critical test of these contrasting hypotheses. Prior research found that among adult Christian religious adherents, intuitions about person psychology coexist and interfere with theological conceptualizations of God (e.g., infallibility). Here, we use a sentence verification paradigm where participants are asked to evaluate as true or false statements on which core knowledge intuitions about person physicality and psychology and Christian theology about God are inconsistent (true on one and false on the other) versus consistent (both true or both false). We find, as predicted by the counterintuitiveness hypothesis but not the Cartesian dualism hypothesis, that Christian religious adherents show worse performance (lower accuracy and slower response time) on statements where Christian theological doctrines about God's physicality (e.g., incorporeality, omnipresence) conflict with intuitions about person physicality. We find these effects for other extraordinary beings in Christianity-the Holy Spirit and Jesus-but not for an ordinary being (priest). We conclude that it is unintuitive to conceptualize extraordinary beings as disembodied, and that this, rather than inherent Cartesian dualism, may explain the prevalence of beliefs in such beings.
Simple Electrical Impedance Tomography Measures for the Assessment of Ventilation Distribution

is the lack of formal training programs in pediatric interventional bronchoscopy. Although further research in larger populations is needed, these findings suggest that adding R-EBUS TBB to standard BAL sampling in immunocompromised children with radiographic opacities considerably improves the microbiologic diagnostic yield. These results represent progress in the emerging field of pediatric interventional pulmonology. n
Force-generating mechanisms of anaphase in human cells

ABSTRACT What forces drive chromosome segregation remains one of the most challenging questions in cell division. Even though the duration of anaphase is short, it is of utmost importance for genome fidelity that no mistakes are made. Seminal studies in model organisms have revealed different mechanisms operating during chromosome segregation in anaphase, but the translation of these mechanisms to human cells is not straightforward. Recent work has shown that kinetochore fiber depolymerization during anaphase A is largely motor independent, whereas spindle elongation during anaphase B is coupled to sliding of interpolar microtubules in human cells. In this Review, we discuss the current knowledge on the mechanisms of force generation by kinetochore, interpolar and astral microtubules. By combining results from numerous studies, we propose a comprehensive picture of the role of individual force-producing and -regulating proteins. Finally, by linking key concepts of anaphase to most recent data, we summarize the contribution of all proposed mechanisms to chromosome segregation and argue that sliding of interpolar microtubules and depolymerization at the kinetochore are the main drivers of chromosome segregation during early anaphase in human cells. Summary: A Review of contributions of force-generating mechanisms, acting through different microtubule populations, to chromosome segregation in human cells.
Paratuberculosis in dairy goat flocks from southern Spain: risk factors associated with seroprevalence

Background Paratuberculosis (PTB) is a chronic, enteric wasting disease of ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP), with a worldwide distribution. Andalusia, located in southern Spain, is one of the European regions with the highest goat census and the highest milk production; however, current data on the prevalence of MAP in this species are not available. Methods A cross-sectional study was performed to determine the seroprevalence and risk factors associated with PTB in dairy goat flocks from southern Spain. A total of 3312 serum samples were collected from 48 flocks located in three different geographical areas. Health and productive parameters were surveyed during the visit to the herds. Results A total of 511 goats were seropositive, with overall true seroprevalence of 22.54 per cent (95 per cent confidence interval (CI95) 21.12–23.97). Of the goat herds, 87.50 per cent (CI9578.14–96.98) were seropositive. The intraherd seroprevalence was 25.43±31.71, distributed as follows: 22 flocks with a seroprevalence under 10 per cent; 18 flocks between 10 per cent and 50 per cent; and eight flocks with a frequency over 50 per cent. Multivariate logistic regression showed significant association between PTB seropositivity and the following variables: intensive production system, lack of management by batches, inappropriate ventilation and seropositivity to c aprine a rthritis e ncephalitis v irus ( CAEV ). Conclusions The results indicate a widespread PTB infection in goat herds in southern Spain. Thus, control programmes must include management and sanitary measures to reduce the prevalence. Further experimental studies are necessary to determine the influence of CAEV-PTB coinfection on immune status.
The Impact of Big Data on Health Care Services in Australia: Using Big Data Analytics to Categorise and Deal with Patients

Big Data is the biggest emerging trend and promise in today's technology-driven world. It is continuing to create a lot of buzz in not only the field of technology, but across the world. It promises substantial involvements, vast changes, modernizations, and integration with and within people's ongoing life. It makes the world more demanding and helps with making prompt and appropriate decisions in real time. This paper aims to provide a comprehensive analysis of the health industry and health care system in Australia that are relevant to the consequences formed by Big Data. This paper primarily uses a secondary research analysis method to provide a wide-ranging investigation into the positive and negative consequences of health issues relevant to Big Data, the architects of those consequences, and those overstated by the consequences. The secondary resources are subject to journal articles, reports, conference proceedings, media articles, corporation-based documents, blogs and other appropriate information. In the future, the investigation will continue by employing Mixed Methodology (Qualitative and Quantitative) in relation to Big Data usage in the Australian Health industry. The paper initially finds that Big Data is an evidence source in health care and provides useful insight into the Australian healthcare system. It is steadily reducing the cost of the Australian healthcare system and improving patients' outcomes in Australia. Big data can not only improve the affairs between public and health enterprises, but can also make life better by increasing efficiency and modernization.
Sentiment Analysis on Twitter Data: A Survey

Twitter is the popular micro blogging site where thousands of people exchange their thoughts daily in the form of tweets. The characteristics of tweet is to be short and simple way of expressions. Though this paper will focus on sentiment analysis of twitter data. The research area of sentiment analysis are text data mining and NLP. In different form we can perform the sentiment analysis on twitter data. This research paper will focus on techniques of sentiment analysis where we will perform how to extract tweets from twitter. Eventually we will compare different sentiment analysis techniques and also the approaches containing twitter dataset.
Maximum Information Gain Relief Feature Weighting

Features selection can improve the accuracy of the model by reducing the number of input features and has become the focus of many researches in areas of application [15]. Feature selection mainly includes two classes: Filter and Wrapper [16]. In this paper, an extensively-used filter method, Relief feature weighting algorithm, is analyzed. In 1990s, Kira proposed the Relief algorithm [10], which is originally designed for binary classification. The test results suggest that Relief has advantages in terms of learning time and learning concept and the method is easy to implement for practice. The iterative Relief algorithm proposed in [12] constructs objective function based on the margin maximization theoretically, it can make up for the lack of clear objective function in the traditional Relief. Afterwards, the EM-like learning strategy is used to derive the weight vector. Recently, Maximum Entropy Relief, which is based on the Maximum Entropy Principle, was proposed in [1] and extended to multi-class classification problems. Local consistency Relief was proposed in [2], and quotes local consistency regularization term. In this paper, we propose the Maximum Information Gain Relief (MIG-Relief) and present the theoretical analysis and case studies. We analyze the mathematical theory and applicability for the proposed method and show the criterion of parameter selection for feature dimensions with cross validation. A new measuring function of fuzzy difference degree and objective function are proposed and followed by the solution of optimization objective function by Lagrange optimization [8].
A Selective Mutation based Fault Localization Method and the Experimental Evaluation

The mutation-based fault localization method can only locate faults related to the mutation operators. If the types of mutation operators are not enough or mutation operators are selected improperly, it will affect the localization accuracy. To solve this problem, the paper proposes four kinds of new mutation operators on the basis of the current mutation operators and implements a selective mutation-based fault localization method. The experimental results on the standard test suite Siemens show that our method has higher effectiveness comparing with Tarantula, LEAP, the state dependency probabilistic-based and joint dependency probabilistic-based method. The proposed mutation operators are proved to be able to locate the faults that the current operators could not locate, and can improve the rank of the faults that the current operators can locate as well.
An Unsupervised Change Detection Approach for Remote Sensing Image Using SURF and Gabor Wavelet Features

In this paper, we propose a novel approach for unsupervised change detection by integrating Speeded up robust features (SURF) key points and Gabor wavelet features. First, SURF key points and Gabor wavelet features are extracted from two temporal remote sensing images respectively. Then, the difference image is generated based on Gabor wavelet features, in which the adjacency between pixels is incorporated to obtain a local relationship. Subsequently, the SURF key points are matched by using Random Sample Consensus (RANSAC) algorithm. The matched key points are viewed as training samples for unchanged class and those for changed class are selected from the remaining SURF key points based on Gaussian mixture model (GMM). Finally, training samples are utilized for training a support vector machine (SVM) classier which is used to segment the difference image into changed and unchanged classes. Experiments employing LandSat images demonstrate the effectiveness of the proposed approach.
Research on the Application of "MOOC" in Modern Distance Education under the Background of Big Data

Starting from the characteristics of the big data era, the researcher reveals the trend of information-based teaching in the big data era, and the changes of resource view, teaching view and teacher development view brought about by the information-based teaching reform, which is of great significance for deepening the reform of modern distance education curriculum and building a new teaching team. Combining with the actual situation of information-based teaching in the era of big data change, it is considered that flipping classroom, MOOC and micro-course are the first wave of data change education. The significant feature of flipping classroom and micro-course is the innovation of information-based teaching in the field of education in the era of cloud computing and big data.
How to Integrate Complex Optimal Data Processing in Information Services in Internet

The information services are widely used in virtual environments. As information services a lot of business entities operate offering e-shop facilities, virtually offering of business information, galleries, many others information services based on distribution of on-line important data. The human requirements to the information services increase by means to expect more complex functionalities, which can be provided by these services. The integration of complex data processing in information systems is a current task, which the everyday business insists and requires. This paper illustrates possible solutions for integrating complex data processing, by applying methods of the control system theories. Information services, which provide optimal problem solutions, incorporating automatic features in business systems and controlling communication systems, are examples, which are given and discussed. The paper provides and analysis of the possible solutions for integrating principals from the control theory by means to provide optimal data processing in information services in Internet.
Physical Property Analysis of Sweet Potatoes Using Computer Vision

This paper demonstrates the performance of using computer vision for sweet potato grading. Different qualities of sweet potatoes can lead to different prices. Better-grade fruits can be sold at a higher price. Therefore, they should be graded before they are transferred to sell. 471 pictures of sweet potatoes are employed for the experiment. The experimental sweet potatoes are divided into four groups. The first group contains good quality with required shapes. The second group also includes good quality roots but undesired shapes. Experimental samples of the third group have no defects, but they are too small, short or thin shaped. Moreover, for the last group sweet potatoes have defects. The samples are inspected from their physical properties including shape, color and texture features. Top-view pictures are captured and used for feature extraction. Various 184 physical properties are extracted. As using a large number of features may cause high computational cost, so important extracted features are selected. The effective features are used for classification based on k-nearest neighbour and neural network theories. The experiments comprise of twenty sub-experiments. About half the numbers of samples are randomly chosen for training sets, the remaining samples being employed for test sets. The experimental results show that in the average of the k-nearest neighbor and neural network classifiers achieve 97.14% and 96.46% accuracy respectively. Nevertheless, the difference of the classifier performances is insignificant that is proved by the paired sample t-test.
A Data-driven Framework for Long-Range Aircraft Conflict Detection and Resolution

At the present time, there is no mechanism for Air Navigation Service Providers (ANSPs) to probe new flight plans filed by the Airlines Operation Centers (AOCs) against the existing approved flight plans to see if they are likely to cause conflicts or bring sector traffic densities beyond control. In the current Air Traffic Control (ATC) operations, aircraft conflicts and sector traffic densities are resolved tactically, increasing workload and leading to potential safety risks and loss of capacity and efficiency. We propose a novel Data-driven Framework to address a long-range aircraft conflict detection and resolution (CDR) problem. Given a set of predicted trajectories, the framework declares a conflict when a protected zone of an aircraft on its trajectory is infringed upon by another aircraft. The framework resolves the conflict by prescribing an alternative solution that is optimized by perturbing at least one of the trajectories involved in the conflict. To achieve this, the framework learns from descriptive patterns of historical trajectories and pertinent weather observations and builds a Hidden Markov Model (HMM). Using a variant of the Viterbi algorithm, the framework avoids the airspace volume in which the conflict is detected and generates a new optimal trajectory that is conflict free. The key concept upon which the framework is built is the assumption that the airspace is nothing more than a horizontally and vertically concatenated set of spatio-temporal data cubes where each cube is considered as an atomic unit. We evaluate our framework using real trajectory datasets with pertinent weather observations from two continents and demonstrate its effectiveness for strategic CDR.
Research on Automatic Tracking Mode of Ship Satellite Communication Based on Kalman Theory

The compound of satellite communication has source antenna to use for the warship ship satellite correspondence words duty, data, fixed position and look for to shout currently etc. business. Carry in the actual warship ship body voyage in, the horizontal shake, carriage variety process in, hard to do to completely aim at to follow satellite, resulting in the signal drift cannot communication. This text is solution follow accuracy problem, deduce engineering and actual data to collect calculate way with Carl Man's mathematics to carry out the exaltation of following the longitude. Solve quickly follow satellite solid of follow longitude problem, the realization correspondence system converses the realization of function.
Towards a rooted subgraph classifier for IoT botnet detection

The Internet of Things (IoT) devices provide various benefits for our modern life. However, in recent years, commercial-off-the-shelf devices such as IP-Camera, Router, Smart-TV, etc. are being targeted more and more by IoT Botnet. Therefore, the detection of IoT botnet malware is essential. Recently, some of the studies have used machine learning and deep learning for the automatic detection of malware. However, machine learning and deep learning also have their own advantages and disadvantages. Therefore, in this paper, we have proposed a method that combine deep learning and machine learning to generate a novel feature-based PSI-Rooted sub-graph for detecting cross-architecture IoT botnet malware. This feature is robust enough for various common machine learning classifiers that achieved an accuracy of about 97% and F-score about 98%.
Computation of Time Series Scaling Exponent for Electricity Prices Forecasting

Electricity is a product traded on the organized markets and has become a commodity having dynamic trade characteristics not typical to other market products. The dynamic of the trade parameters is due to the fact that this product is non-storable, need to have a constant balance between production and consumption as well as strong influence by external weather conditions, neighboring markets and dependence of the consumption on hourly, daily, weekly and monthly basis. The basic characteristics - market prices of the electricity have complex nature, variability and functional dependence of weather, market and system operation, supply and demand, costs of transportation in the grid, regional specifics as intensity of industry and government interventions. One of the most important characteristics is the day-ahead electricity prices, which usually are used as a reference for forecasting and risk management. The scientific research of the using of different mathematical methods and models for forecasting the dynamics of the day or weekly-ahead electricity prices is an important scientific issue. The aim of this paper is to present a scientific approach for electricity prices forecasting, via the analysis of the time series using the mathematical method of Hurst parameter, called the rescaled range (R/S) method. This method is used for distinguishing completely random time series from correlated time series in process of modeling and forecasting of day-ahead electricity market. The mathematical analysis is based on the information from the Bulgarian day-ahead electricity market.