Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex and Death: The Effects of Innate Immune Factors on the Sexual Reproduction of Malaria Parasites

Malaria parasites must undergo a round of sexual reproduction in the blood meal of a mosquito vector to be transmitted between hosts. Developing a transmission-blocking intervention to prevent parasites from mating is a major goal of biomedicine, but its effectiveness could be compromised if parasites can compensate by simply adjusting their sex allocation strategies. Recently, the application of evolutionary theory for sex allocation has been supported by experiments demonstrating that malaria parasites adjust their sex ratios in response to infection genetic diversity, precisely as predicted. Theory also predicts that parasites should adjust sex allocation in response to host immunity. Whilst data are supportive, the assumptions underlying this prediction – that host immune responses have differential effects on the mating ability of males and females – have not yet been tested. Here, we combine experimental work with theoretical models in order to investigate whether the development and fertility of male and female parasites is affected by innate immune factors and develop new theory to predict how parasites' sex allocation strategies should evolve in response to the observed effects. Specifically, we demonstrate that reactive nitrogen species impair gametogenesis of males only, but reduce the fertility of both male and female gametes. In contrast, tumour necrosis factor-α does not influence gametogenesis in either sex but impairs zygote development. Therefore, our experiments demonstrate that immune factors have complex effects on each sex, ranging from reducing the ability of gametocytes to develop into gametes, to affecting the viability of offspring. We incorporate these results into theory to predict how the evolutionary trajectories of parasite sex ratio strategies are shaped by sex differences in gamete production, fertility and offspring development. We show that medical interventions targeting offspring development are more likely to be ‘evolution-proof’ than interventions directed at killing males or females. Given the drive to develop medical interventions that interfere with parasite mating, our data and theoretical models have important implications.
Are The Integrations Between Ontologies and Databases Really Opening the Closed World in Ubiquitous Computing ?

Currently, ontologies are widely used in ubiquitous systems for the representation of context and situation information. In addition, task ontologies are used to describe processes and daily situations that may occur in certain domains. In this way, it is necessary that there are strategies of integration between ontologies and databases, because ubiquitous systems generate large amounts of context data. In addition, it is necessary that exist ways to query and infer information on these persisted ontologies. Through the description of a motivating scenario, based on ClinicSpace ubiquitous architecture, and the study of requirements identified on recently proposed architectures, it was possible to list a set of important features to use persistence tools in ubiquitous systems. This paper presents an analysis of recently proposed solutions for the use of ontologies as data model for context representation and situation inference in ubiquitous systems. Through this analysis was possible to list a set of requirements that the actual state of the art does not attend, such as the possibility of distributed inference about information in ontologies.
Privacy preservation in transaction databases based on anatomy technique

This paper considers the problem of privacy preserving transaction data publishing. Transaction data are usually useful for data mining. While it is high-dimensional data, traditional anonymization techniques such as generalization and suppression are not suitable. In this paper, we present a novel technique based on anatomy technique and propose a simple linear-time anonymous algorithm that meets the l-diversity requirement. The simulation experiments on real datasets and the results of association rules mining on the anonymous transaction data showed that our algorithm can safely and efficiently preserve the privacy in transaction data publication, while ensuring high utility of the released data.
A Search for the Optical Counterpart to the Magnetar CXOU J010043.1–721134

After our tentative detection of an optical counterpart to CXOU J010043.1–721134 from archival Hubble Space Telescope (HST) imaging, we have followed up with further images in four bands. Unfortunately, the source originally identified is not confirmed. We provide deep photometric limits in four bands and accurate photometry of field stars around the location of the magnetar.
Investigation into Alternative Representations for Intelligent Geoinformation Systems in Data-rich Environments

Whilst a geographical information system may use many different representations of areal data, the vector format is the most commonly utilized structure. Although some researchers consider it efficient, others comment on its drawbacks. The potential exists for the implementation of another format with improved attributes and capabilities. This paper presents a method of creating a hybrid Voronoi approach to areal representation. This representation builds the positive qualities of the vectorized model into the Voronoi diagram to improve upon functionality within a geoinformation environment. This alternative representation aims to offer notable performance improvements whilst maintaining comparable accuracy to its original counterpart. A number of important applications are summarized, as are potential extensions of this work.
Topic-based recommendations in enterprise social media sharing platforms

Nowadays, many companies deploy social media technologies to foster the knowledge transfer in the enterprise. As the amount of available content in such systems grows, there is an increasing need for recommender systems that provide recommendations according to the knowledge workers' needs and preferences. We propose a topic-based recommender system for Enterprise 2.0 resource sharing platforms. The system identifies the knowledge workers' short-term and long-term topics of interest by applying algorithms from the domain of topic detection and tracking and generates recommendations with a high degree of inter-topic diversity.
The Method of Case Retrieving in the Emergency Field Based on CBR

How to make scientific and effective emergency decisions is a research hotspot in academic circles at present. This paper applies case-based reasoning to emergency aid decision-making, which provides a method of scientific and effective aid decision-making for the emergency leaders. The two-layer case retrieving algorithm based on structural similarity degree and attribute similarity degree is designed, which effectively overcomes the shortcomings of traditional Nearest Neighbor Algorithm that fails to calculate the similarities between the cases with the missing values. The application of case retrieving algorithm to the field of typhoon analysis is used to illustrate the practicality of case retrieving in emergency aid decision-making.
Multi-robot formation control using leader-follower for MANET

With the development of the multi-robot coordination, the working efficiency of the multi-robot system is improved, and the tasks can be finished better. Multi-robot coordination is one of the important issues for the research of mobile robot. The research of formation control of multi-robot will improve the efficiency of the coordination of multi-robot. So the problem of multi-robot formation is a typical problem of the system of robot team formation, and also communications play an important role in mobile robot systems able to address real world applications. Mobile ad-hoc networks are characterized by self-organization, rapid deployment and fault tolerance. A multi-robot formation supported by mobile Ad-hoc networks is suitable to some special situations where the communication devices of mobile networks cannot be preinstalled. The technology of leader-follower method has been very mature and the effect is good, and could be fast respond. However one issue with the typical leader-follower strategy is the lack of inter-robot information feedback throughout the group. And at present, the research of formation control only based on communication in real-time is not high, and the effect is bad. As the reason mentioned above, in this paper, we combined the leader-follower method with ad-hoc network to solve the problem of real-time formation. Then the robots can be in their lines correctly and quickly and arrive at the goal position. Computerized simulation results show that the approach is feasible.
Digital Signature on Mobile Devices based on Location

With rapid growing of wireless and mobile technologies, mobile devices are part and parcel of our lives. Nowadays GPS (Global Positioning Service) technology is combined with mobile devices. People use some Internet applications, e.g. mobile fund transfer, e-commerce, game online and theatre booking. These applications should support digital signature service for more security. Because mobile device is low-computation capability and limited battery life, it is not suitable to use digital signature protocols, which are based on asymmetric cryptographic algorithm, with mobile devices. And it does not use advantage of GPS technology. We present "location based digital signature on mobile devices" with geo-encryption to generate digital signature. This method reduces the burden on mobile devices, the computation time of the requester by almost 98%, compared with Abe-Fujisaki's RSA-based partially blind signature and decreases communication between sender and receiver
An Autonomous Trust Construction System Based on Bayesian Method

Though research on multi-agent systems have progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the multi-agent system is a large, uncensored system. This raises the question of how much dependability to give each information source. We cannot expect each user to know the trustworthiness of each source. Trust mechanism is regarded as a key for solving the problem of security in multi-agent systems. To achieve the trust management autonomy, this paper presents an autonomous trust construction model based on Bayesian method. The presented model adopts the Bayesian method to evaluate the trust value of each agent, and uses the graph theory to combination and path searching to construct the trust relation. The simulation experiment results prove that the autonomous trust construction is effective.
From Entities to Geometry: Towards exploiting Multiple Sources to Predict Relevance

The goal of an Information Retrieval (IR) system is to predict which information objects can help users in satisfying their information needs, i.e. predict relevance. Different sources of evidence can be exploited for this purpose. These sources are the properties of the different entities involved when retrieving and accessing information, where examples of entities include the information objects, the task, the user, or the location. The main hypothesis of this paper is that, to exploit the variety of entities and sources, it is necessary to model the relationships existing between the entities and those existing between the properties of the entities. Such relationships are themselves possible sources that can be used to predict relevance. This paper proposes a methodology that supports the design of an IR system able to model in a uniform way the properties of the entities involved, the properties of their relationships and the relationships between the different properties. The methodology is structured in four steps, aiming, respectively, at supporting the selection of the sources, collecting the evidence, modeling the sources and their relationships, and using the latter two to predict relevance. Sources and relationships are modeled and then exploited through a previously proposed geometric framework, which provides a uniform and concrete representation in terms of vector subspaces.
Optimal Control with Finite Feedback Data Rate

The problem of stabilizing a discrete time linear system by a limited rate feedback is solved in the literature using various approaches, all yielding the same result in term of the minimum rata rate required to obtain the stabilization. In this contribution we will propose an optimal control strategy which enables to improve the performance of the control system which is based of a randomization of the quantizer
Risk aversion and the Pareto frontier of a dynamic principal-agent model: an evolutionary approximation

In this paper we formulate an infinitely repeated Principal-Agent relationship as a Multi-Objective Optimization problem. We numerically approximate the solution of this model using a Multi-Objective Optimization Evolutionary Algorithm, named RankMOEA, for different values of the Agent's coefficient of relative risk aversion. Our numerical results indicate that as the Agent becomes more risk averse, the Pareto Frontier becomes less concave, the Principal-Agent relationship generates more value for the Agent, and the Principal appears to assume more of the risk sharing regardless of the contract we analyze along the Pareto Frontier.
HIF1-Alpha Expression Predicts Survival of Patients with Squamous Cell Carcinoma of the Oral Cavity

Background Oral squamous cell carcinoma is an important cause of death and morbidity wordwide and effective prognostic markers are still to be discovered. HIF1α protein is associated with hypoxia response and neovascularization, essential conditions for solid tumors survival. The relationship between HIF1α expression, tumor progression and treatment response in head and neck cancer is still poorly understood. Patients and Methods In this study, we investigated HIF1α expression by immunohistochemistry in tissue microarrays and its relationship with clinical findings, histopathological results and survival of 66 patients with squamous cell carcinoma of the lower mouth. Results Our results demonstrated that high HIF1α expression is associated with local disease-free survival, independently from the choice of treatment. Furthermore, high expression of HIF1α in patients treated with postoperative radiotherapy was associated with survival, therefore being a novel prognostic marker in squamous cell carcinoma of the mouth. Additionally, our results showed that MVD was associated with HIF1α expression and local disease relapse. Conclusion These findings suggest that HIF1α expression can be used as a prognostic marker and predictor of postoperative radiotherapy response, helping the oncologist choose the best treatment for each patient.
Secured e-health data retrieval in DaaS and Big Data

Big Data is one of rising IT trends such as cloud computing, social networking or ubiquitous computing. Big Data can offer beneficial scenarios in the e-health arena. However, one of the scenarios can be that Big Data needs to be kept secured for a long time in order to gain its benefits such as finding cures for infectious diseases and keeping patients' privacy. From this connection, it is beneficial to analyze Big Data to make meaningful information while the data are stored in a secure manner. Thus, the analysis of various database encryption techniques is essential. In this study, we simulated 3 types of technical environments such as Plain-text, Microsoft Built-in Encryption, and custom Advanced Encryption Standard using Bucket Index in Data-as-a-Service. The results showed that custom AES-DaaS has faster range query response time than MS built-in encryption. In addition, while carrying out the scalability test, we acknowledged there are performance thresholds according to physical IT resources. Therefore, for the purpose of efficient Big Data management in e-health, it is noteworthy to examine its scalability limits as well even if it is under cloud computing environment. Furthermore, when designing an e-health database, both patients' privacy and system performance needs to be dealt as top priorities.
On LM Heuristics for the Cube Growing Algorithm

Current approaches to statistical machine translation try to incorporate more structure into the translation process by including explicit syntactic information in form of a formal grammar (with a possible, but not necessary, correspondence to a linguistic motivated grammar). These more structured models incur into an increased generation cost, and efficient algorithms must be developed. In this paper we concentrate on the cube growing algorithm, a lazy version of the cube grow algorithm. The efficiency of this algorithm depends on a heuristic for language model computation, which is only scarcely discussed in the original paper. In this paper we investigate the effect of this heuristic on translation performance and efficiency and propose a new heuristic which efficiently decreases memory requirements and computation time, while maintaining translation performance.
Utilizing GQM+Strategies for an Organization-Wide Earned Value Analysis

In this paper, we offer an approach for performing an organization-wide earned value analysis by taking advantage of the hierarchical structure of the GQM+Strategies grid. The merger enables us to create an integrated hierarchy of business goals, value goals, and strategies for achieving those goals and to monitor and evaluate those goals at all levels. It provides a means to expand the definition of earned value metrics to cover both the costs and benefits of achieving those goals through those strategies and provides measurement support for all concepts.
Shape invariant time-scale modification of speech using a harmonic model

A new and simple approach to shape invariant time-scale modification of speech is presented. The method based upon a harmonic coding of each speech frame operates entirely within the original sinusoidal model and makes no use of "pitch-pulse onset times" used by conventional algorithms. Instead, phase coherence, and thus shape invariance, are ensured by exploiting the harmonic relation existing between the sine waves to cause them to be in phase at each adjusted frame boundary. Results suggest this approach to be an excellent candidate for use within a concatenative text-to-speech synthesiser where scaling factors typically lie within a range well handled by this algorithm.
A Comparative Evaluation of Conventionnal and Supercapacitors in Grid-Connected Transformerless PV Systems

The fluctuations in the power delivered to the grid remain a problem in photovoltaic grid-connected systems. Energy storage systems are proposed as a solution in order to comply with these standard power quality requirements. Due to their distinctive properties, supercapacitors can be considered as the most efficient energy storage systems available in the market today. Thus, their capability of fast charging and discharging and their high capacitance (hundred of farads) compared to the traditional ones have been the main reason of their massive integration in PV systems. This paper presents a comparison between a conventional capacitor and a supercapacitor used as DC-link in a PV grid connected system. The superiority of the supercapacitor is demonstrated through a performance evaluation based on simulation results.
A Region Merging Prior for Variational Level Set Image Segmentation

In current level set image segmentation methods, the number of regions is assumed to known beforehand. As a result, it remains constant during the optimization of the objective functional. How to allow it to vary is an important question which has been generally avoided. This study investigates a region merging prior related to regions area to allow the number of regions to vary automatically during curve evolution, thereby optimizing the objective functional implicitly with respect to the number of regions. We give a statistical interpretation to the coefficient of this prior to balance its effect systematically against the other functional terms. We demonstrate the validity and efficiency of the method by testing on real images of intensity, color, and motion.
Genome-wide scan for schizophrenia in the Finnish population : evidence for a locus on chromosome 7 q 22

We report the results of a four-stage genome-wide scan in a schizophrenia study sample consisting of 134 affected sib-pairs collected in Finland. In stage I we genotyped 370 markers from the Weber 6 screening set (N = 52 affected sib-pairs); in stage II we followed up 40 markers by typing first-degree relatives of the sib-pairs; in stage III we genotyped 15 markers in 134 families; and in stage IV we genotyped a denser marker map in the two most promising regions, one on chromosome 1 and another on chromosome 7, in all families. Diagnoses were based on three nationwide health care registers and consensus diagnosis based on review of all medical records. The most significant finding was a two-point lod score of 3.18 with marker D7S486 using a dominant model and treating all individuals with either schizophrenia, schizoaffective disorder or other schizophrenia spectrum disorder as affected. Multipoint analysis with MAPMAKER/SIBS resulted in a MLS of 3.53 between markers D7S501 and D7S523 using the broadest diagnostic model, including major depressive disorder and bipolar type I as affecteds in addition to the aforementioned phenotypes. These results were obtained by including in the analyses only individuals from the late settlement region of Finland settled in the 16th century. Additionally, some support was obtained for linkage to chromosome 1, in a region previously identified in a genome-wide scan of a study sample from a sub-isolate of Finland. Our data demonstrate the importance of genealogical information for studies aiming at identification of predisposing loci in complex diseases. INTRODUCTION
A simple 2-transistor transresistor

A simple transresistance amplifier is introduced that is both linear and compact. It consists of only two transistors and a current source. Despite its simplicity, its linearity properties are attractive when compared to existing transresistance structures. Simulation results comparing the performance of the new structure with several other single-ended and balanced structures are presented along with experimental results.
Morning Reattachment to Work and Work Engagement During the Day: A Look at Day-Level Mediators

Reattachment to work (i.e., rebuilding a mental connection to work) before actually starting work is important for work engagement during the day. Building on motivated action theory, this study examines anticipated task focus, positive affect, and job resources (job control and social support) as mediators that translate reattachment in the morning into work engagement during the day. We collected daily-survey data from 151 employees (total of 620 days) and analyzed these data with a multilevel path model. We found that day-level reattachment to work in the morning predicted anticipated task focus, positive affect, social support, and job control through goal activation and that anticipated task focus, positive affect, social support, and job control predicted work engagement during the day. This study points to the important role of reattachment to work in employee experiences and behaviors throughout the workday and specifically highlights the benefits of such initial mental boundary crossing between life domains for employee engagement at work.