Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Characterization of Rhoptry Kinome in the Virulent Toxoplasma gondii RH Strain

Toxoplasma gondii is an obligatory intracellular apicomplexan protozoan which can infect any warm-blooded animal and causes severe diseases in immunocompromised individuals or infants infected in utero. The survival and success of this parasite require that it colonizes the host cell, avoids host immune defenses, replicates within an appropriate niche, and exits the infected host cell to spread to neighboring non-infected cells. All of these processes depend on the parasite ability to synthesis and export secreted proteins. Amongst the secreted proteins, rhoptry organelle proteins (ROPs) are essential for the parasite invasion and host cell manipulation. Even though the functions of most ROPs have been elucidated in the less virulent T. gondii (type II), the roles of ROPs in the highly virulent type I strain remain largely un-characterized. Herein, we investigated the contributions of 15 ROPs (ROP10, ROP11, ROP15, ROP20, ROP23, ROP31, ROP32, ROP33, ROP34, ROP35, ROP36, ROP40, ROP41, ROP46, and ROP47) to the infectivity of the high virulent type I T. gondii (RH strain). Using CRISPR-Cas9, these 15 ROPs genes were successfully disrupted and the effects of gene knockout on the parasite’s ability to infect cells in vitro and BALB/c mice in vivo were investigated. These results showed that deletions of these ROPs did not interfere with the parasite ability to grow in cultured human foreskin fibroblast cells and did not significantly alter parasite pathogenicity for BALB/c mice. Although these ROPs did not seem to be essential for the acute infectious stage of type I T. gondii in the mouse model, they might have different functions in other intermediate hosts or play different roles in other life cycle forms of this parasite due to the different expression patterns; this warrants further investigations.
World wide web crawler

ABSTRACT We describe our ongoing work on world wide web crawling, a scalable web crawler architecture that can use resources distributed world-wide. The architecture allows us to use loosely managed compute nodes (PCs connected to the Internet), and may save network bandwidth significantly. In this poster, we discuss why such architecture is necessary, point out difficulties in designing such architecture, and describe our design in progress. We also report on our experimental results that support the potential of world wide web crawling. Keywords Crawling, Distributed Computing, P2P 1. INTRODUCTION Today's search engines move web data from the world to one place. They use a server-client architecture where the central server manages all the status information (URLs visited and to visit) [2,3]. This architecture requires a significant amount of compute and network resources at the crawling site, so only a small number of organizations can afford to operate them. Even with today's state-of-the-art search engines [1], a single round of crawling and indexing takes a month or more to cover a significant portion of web data. As a consequence, they cannot provide up-to-date version of frequently updated pages. To catch up frequent updates without putting a large burden on contents provider, we believe
Entrepreneurial Intention among Syrian Students

In hard times, when educated persons cannot get job, self-employment is referred as a solution. But entrepreneurship is not the function that might be outcome of simple efforts. It requires a regular and permanent attitude as a part of personality. Few studies have examined how personality differences among individual's students can influence their entrepreneurial intention especially among students enrolled in Business Administration programs This paper aims to examine the relationship between personality traits using big five factor model and entrepreneurial intention. A total sample of 152 students from business schools in Syria is selected. The research finding indicates that there is a significant positive impact of openness to experience and extraversion on entrepreneurial intention.
Global optimisation model and algorithm for unit restarting sequence considering black‐start zone partitioning

Unit restarting after a blackout is the basis of the overall restoration process for a power system. A reasonable restarting sequence of units may speed up the restoration process and reduce economic loss. In view of the situation that there are usually several black-start sources in a large-scale power system, a global optimisation method for unit restarting considering black-start zone partitioning is proposed in this study. On the basis of the multiple time-step modelling framework, the optimisation problem is formulated as a mixed integer linear programming model, with the objective functions to maximise the total-weighted MWh output and minimise the links between the subsystems under various operational constraints. The hierarchical method based on generalised Benders decomposition is applied to decompose the optimisation problem into one master problem and two sub-problems: (i) determination of the unit restarting sequence considering only the grouping of units and (ii) rationality check of unit grouping and check of operational constraints based on power flow calculation. The master problem and sub-problems are alternately and iteratively solved until the optimal solution is obtained. The results of New England 10-unit 39-bus system and a practical power system in southwest China verify the validity of the proposed method.
Static Magnetic Field Stimulation over the Visual Cortex Increases Alpha Oscillations and Slows Visual Search in Humans

Transcranial static magnetic field stimulation (tSMS) was recently introduced as a promising tool to modulate human cerebral excitability in a noninvasive and portable way. However, a demonstration that static magnetic fields can influence human brain activity and behavior is currently lacking, despite evidence that static magnetic fields interfere with neuronal function in animals. Here we show that transcranial application of a static magnetic field (120–200 mT at 2–3 cm from the magnet surface) over the human occiput produces a focal increase in the power of alpha oscillations in underlying cortex. Critically, this neurophysiological effect of tSMS is paralleled by slowed performance in a visual search task, selectively for the most difficult target detection trials. The typical relationship between prestimulus alpha power over posterior cortical areas and reaction time (RT) to targets during tSMS is altered such that tSMS-dependent increases in alpha power are associated with longer RTs for difficult, but not easy, target detection trials. Our results directly demonstrate that a powerful magnet placed on the scalp modulates normal brain activity and induces behavioral changes in humans.
A szénhidrátok táplálkozás-élettani hatásaiés a sporttáplálkozásban betöltött szerepük = The nutritional and physiological effect ofcarbohydrates, their influences on sports nutrition

A szenhidratok olyan makronutriensek, melyek a sejtek elsődleges energiaforrasai. A becsult atlagos szuksegletet az agy glukozfelhasznalasa alapjan szamoljak, igy a javasolt minimum beviteli mennyiseg 130 g/nap felnőttek szamara. A nem emeszthető szenhidratok koze a dietas rostok tartoznak, melyek előnyos fiziologias hatasokkal birnak az emberi szervezet szamara, befolyassal vannak a glikemias kontrollra, diabetesre es verzsirszintekre. Az elegseges rostbeviteli erteket 25-28 g/nap kozott hataroztak meg a 19-50 eves felnőttek szamara. A megfelelő szenhidratbevitel nagy figyelmet kap a sporttaplalkozas teruleten, ugyanis nagy intenzitasu edzes eseten az izomglikogen nyujtja a legfőbb energiaforrast. Sportolok szamara a javasolt napi szenhidrat mennyisege altalaban 5-12 g/ttkg. A verseny előtti szenhidrattoltes 36-48 oraval a megmerettetes előtt elkezdődik, melynek pontos szenhidrat- beviteli javaslata fugg a terheles időtartamatol es intenzitasatol. A testedzes időbeni terjedelme fontos tenyező abbol a szempontbol, hogy kell-e szenhidratot potolni sportolas kozben vagy sem.
Aquatic suction feeding dynamics: insights from computational modelling

Aquatic suction feeding in vertebrates involves extremely unsteady flow, externally as well as internally of the expanding mouth cavity. Consequently, studying the hydrodynamics involved in this process is a challenging research area, where experimental studies and mathematical models gradually aid our understanding of how suction feeding works mechanically. Especially for flow patterns inside the mouth cavity, our current knowledge is almost entirely based on modelling studies. In the present paper, we critically discuss some of the assumptions and limitations of previous analytical models of suction feeding using computational fluid dynamics.
Image Denoising using Adaptive Thresholding in Framelet Transform Domain

Noise will be unavoidable during image acquisition process and denosing is an essential step to improve the image quality. Image denoising involves the manipulation of the image data to produce a visually high quality image. Finding efficient image denoising methods is still valid challenge in image processing. Wavelet denoising attempts to remove the noise present in the imagery while preserving the image characteristics, regardless of its frequency content. Many of the wavelet based denoising algorithms use DWT (Discrete Wavelet Transform) in the decomposition stage which is suffering from shift variance. To overcome this, in this paper we proposed the denoising method which uses Framelet transform to decompose the image and performed shrinkage operation to eliminate the noise . The framework describes a comparative study of different thresholding techniques for image denoising in Framelet transform domain. The idea is to transform the data into the Framelet basis, example shrinkage followed by the inverse transform. In this work different shrinkage rules such as universal shrink(US),Visu shrink (VS), Minmax shrink(MS), Sure shrink(SS) , Bayes shrink(BS) and Normal shrink(NS) were incorporated . Results based on different noise such as Gausssian noise, Poission noise , Salt and pepper noise and Speckle noise at (�� =10,20) performed in this paper and peak signal to noise ratio (PSNR) and Structural similarity index measure(SSIM) as a measure of the quality of denoising was performed.
Developing Data Allocation Schemes by Incremental Mining of User Moving Patterns in a Mobile Computing System

In this paper, we present a new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality of results obtained. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two schemes are devised: one utilizes the set level of moving patterns and the other utilizes their path level. Schemes for shared data are also developed. Performance of these schemes is comparatively analyzed.
Social relation extraction with improved distant supervised and word embedding features

With the rapid development of Internet, how to extract personal relations from Internet has become an important research topic in information extraction. However, current relation extraction researches mainly focus on the processing of English language, the researches focus on Chinese are less. At the same time, there are two main problems in current personal relation extraction approaches: 1) it is difficult to get a large amount of high quality training data without manually label effort; 2) the performance of personal relation extraction from Chinese is unsatisfactory. To solve the first problem, we propose an improved distant supervision method which is applied to Chinese language and can label large-scale of high quality training data automatically. To solve the second problem, we extract three features based on word embedding and combine them with the basic features. In the experiment, the improved distant supervision methods improve the quality of training data significantly. And the word embedding features improve the average F1 score by 4% than the basic features.
Applying the Hilbert-Huang decomposition to horizontal light propagation Cn2 data

The Hilbert Huang Transform is a new technique for the analysis of non-stationary signals. It comprises two distinct parts: Empirical Mode Decomposition (EMD) and the Hilbert Transform of each of the modes found from the first step to produce a Hilbert Spectrum. The EMD is an adaptive decomposition of the data, which results in the extraction of Intrinsic Mode Functions (IMFs). We discuss the application of the EMD to the calibration of two optical scintillometers that have been used to measure Cn2 over horizontal paths on a building rooftop, and discuss the advantage of using the Marginal Hilbert Spectrum over the traditional Fourier Power Spectrum.
Interest points based collaborative tracking

In this paper, we propose a robust collaborative tracking algorithm based on interest points detection and template matching in sparse representation framework. In the proposed tracker, the target dictionary and the candidate dictionary are constructed with the patches around interest points of the previous frame and the current frame, respectively. The correspondence between target points and candidate points is computed by solving an Li minimization problem. Only the mutually matched target and candidate point pairs are selected, and the displacements of them are measured to generate the candidate targets in current frame. To find the best one among all candidate targets, each of them is sparsely represented by target templates given as benchmarks in initial frame. The candidate target with the smallest projection error produces the final tracking result. The experimental results show that the proposed tracker is superior to the state-of-the-art methods remarkably with respect to the tracking accuracy.
Consumption Pattern Recognition System Based on SVM

In this paper, we present a consumption pattern recognition system based on SVM. It can produce an optimized classification pattern using SVM algorithm and use the pattern to predict consumer behaviors. In this system, three dimension reduction methods including Principal Component Analysis (PCA), correlation analysis and data cubes are applied to reduce dimension of features and two training methods including Support Vector Machine (SVM) and Support Vector Machine by Increasing Negative Examples (SVM-INE) are utilized to build classifiers. Consumption pattern recognition system can find the consumption habits of specific consumer group which are helpful to well-targeted marketing. Empirical results show that the system can recognize different consumption pattern with high efficiency and accuracy.
Reset control of a liquid level process

The aim of this work is to present an experiment of liquid level process controlled by a reset PI+CI compensator. The results are compared with a well tuned linear PI compensator, showing that the reset compensator improves the performance in both setpoint tracking and disturbance rejection. The experiment validates the simulation results obtained for a tuning rule proposed in a previous work.
Security-Efficient Routing For Highly Dynamic MANETS

The Goal of Position-based Opportunistic Routing (POR) is to solve the problem of delivering data packets for highly dynamic mobile adhoc networks in a timely manner. The protocol (POR) takes the property of Geographic routing. Here, the data packets are sent out from the source node and some of the neighbor nodes will be the forwarding candidates, if the best forwarder did not forward the packet in a particular period of time; the forwarding candidates will forward the packets. By using Virtual Destination-based Void Handling (VDVH) Scheme, the communication hole is avoided. In the existing system, the geographic routing property is used, the problem of delivering data packets for highly Dynamic mobile adhoc networks is solved. But there is a limitation that the nodes that is selected as the best forwarder is not checked whether it is secured or not. To overcome this problem a Security-efficient routing is proposed in which the nodes which have the higher trust value is considered as the best forwarder. The Selfish and normal nodes is differentiated by using the RREQ algorithm. The Selfish nodes do not forward the request. It will check the trust value, whether it is friend or stranger or acquaintances. In this approach, From the RREQ algorithm, Xrf, Xra, Xrs are the threshold values set for friends, acquaintances and strangers, as per the requirements of the application software. Random waypoint model is chosen as the movement.
An approach for robust mapping, detection, tracking and classification in dynamic environments

Understanding its environment remains a difficult problem for a mobile robot. Several intricate problems (localization, mapping, detection, tracking, classification) have indeed to be solved concurrently. However, most perception algorithms solve these issues independently leading to limited performances in highly changing environments. We present in this paper an original approach where the mapping, the tracking, the detection and the classification problems are addressed concurrently and where the perceptual knowledge of the robot is described using four recursively estimated discrete probability mass functions. Our first experiments based on simulated and real range data show that our approach is able to cope with complex outdoor situations.
Electrical energy consumption forecasting in oil refining industry using support vector machines and particle swarm optimization

In this paper, Support Vector Machines (SVMs) are applied in predicting electrical energy consumption in the atmospheric distillation of oil refining at a particular oil refinery. During cross-validation process of the SVM training Particle Swarm Optimization (PSO) algorithm was utilized in selection of free SVM kernel parameters. Incorporation of PSO into SVM training process has greatly enhanced the quality of prediction. Furthermore, various (different) kernel functions were used and optimized in the process of forming the SVM models.
Enzyme replacement therapy for Fabry disease: A systematic review and meta-analysis

The specific treatment available for Fabry disease (FD) is enzyme replacement therapy (ERT) with agalsidase alfa or beta. A systematic review and meta-analysis was conducted to assess the efficacy and safety of ERT for FD. Only double-blind, randomized clinical trials (RCTs) comparing agalsidase alfa or beta and placebo were included. ERT with either agalsidase alfa or beta was considered similar for the purposes of analysis. Ten RCTs were identified, which showed improvements in neuropathic pain, in heart abnormalities and in globotriaosylceramide (GL-3) levels. A meta-analysis showed increased odds for fever, rigors, development of IgG antibodies to agalsidase, and no significant association with development of hypertension or reduction in the QRS complex duration on electrocardiogram. The RCTs included in this comparison enrolled few patients, were highly heterogeneous, and were focused mainly on surrogate endpoints, limiting any conclusions as to the real effect of ERT for FD. The available evidence suggests that response to ERT is variable across patient subgroups and that agalsidase may slow progression of FD, with slight improvement of existing changes. Nevertheless, many uncertainties remain, and further studies are necessary.
Marginal Construction of Stationary Time Series with applicationto Volatility

In this paper, we provide a new method for modelling stationary time series, concentrating on volatility models. Knowledge about the marginalfamily for the observables is usually quite speciic, for example, exchange rate returns are thought to follow a Student-t. Our approach is to construct the model with a speciied marginal family and build the dependence structure around it. In particular, we present a novel application of the Gibbs sampler.
Linear Cellular Automata and Finite Automata

Linear cellular automata have a canonical representation in terms of labeled de Bruijn graphs. We will show that these graphs, construed as semiautomata, provide a natural setting for the study of cellular automata. For example, we give a simple algorithm to determine reversibility and surjectivity of the global maps. We also comment on Wolfram’s question about the growth rates of the minimal finite state machines associated with iterates of a cellular automaton.
Foundations for Decision Problems in String Logic with Equations, Regular Membership and Length Constraints (Technical Report)

We establish foundational results on the satisfiability problem in string logic with equations, regular membership and Presburger constraints over length functions. We show that the problem of solving string formulas in the logic is decidable. The essence of our decision procedure is a sound and complete algorithm to enumerate an equivalent set of solvable disjuncts for a string formula. We further provide a complexity analysis of our decision procedure to show that it runs, in the worst case, in doubly exponential time. Finally, we present a subfragment where the satisfiability problem is in fractional time.
Original Paper

The introduced feral cat (Felis catus) is a widespread generalist with flexible social behaviour and an apex predator without major interspecific competitors in insular ecosystems that evolved in the absence of predators. Mech-anistic definitions consider an animal's home range to be the spatial expression of a cognitive map that is kept up-to-date with the status of critical resources that contribute to animal fitness. We assumed there are two major determinants struc-turing the home range of cats as apex predators in insular ecosystems: the distribution of critical food resources and conspecific distribution. We hypothesized that cats structure their home ranges by optimizing the use of staple critical food resources and that as a consequence of the presence of rich resources cats tend to socialise, aggregate and share space. We carried out spatial analyses using location data for feral cats tracked using lightweight GPS collars in conjunction with the suitability value of rabbit patches and their associated ownership costs for cats within a New Zealand braided-river environment. Cat home ranges and spatial distribution , especially for females, were related to the inclusion of rabbit patches within home ranges with higher mean value than the average of neighbourhood patches in the landscape. Cats showed solitary behaviour but tolerance to conspecific presence by sharing high-use areas and high-value rabbit patches, mostly at different times, resulting in occasional encounters among males and females. Home range size and patterns of spatial overlap were dependant on sex and season. Solitary spacing patterns as consequence of innate preferences together with resource constraints may regulate feral cat population densities.