Miracle-dz/UniFAR-FTUs huggingface dataset:
XMLSchema2ShEx: Converting XML validation to RDF validation

RDF validation is a field where the Semantic Web community is currently focusing attention. Besides, there is a recent trend to migrate data from different sources to semantic web formats. Therefore, in order to facilitate this transformation, we propose: a set of mappings that can be used to convert from XML Schema to Shape Expressions (ShEx), a prototype that implements a subset of the proposed mappings, an example application to obtain a ShEx schema from an XML Schema and a discussion on conversion implications of non-deterministic schemata. We demonstrate that an XML and its corresponding XML Schema are still valid when converted to their RDF and ShEx counterparts. This conversion, along with the development of other format mappings, could drive to an improvement of data interoperability due to the reduction of the technological gap.
Actin from the apicomplexan Neospora caninum (NcACT) has different isoforms in 2D electrophoresis

Abstract Apicomplexan parasites have unconventional actins that play a central role in important cellular processes such as apicoplast replication, motility of dense granules, endocytic trafficking and force generation for motility and host cell invasion. In this study, we investigated the actin of the apicomplexan Neospora caninum – a parasite associated with infectious abortion and neonatal mortality in livestock. Neospora caninum actin was detected and identified in two bands by one-dimensional (1D) western blot and in nine spots by the 2D technique. The mass spectrometry data indicated that N. caninum has at least nine different actin isoforms, possibly caused by post-translational modifications. In addition, the C4 pan-actin antibody detected specifically actin in N. caninum cellular extract. Extracellular N. caninum tachyzoites were treated with toxins that act on actin, jasplakinolide and cytochalasin D. Both substances altered the peripheric cytoplasmic localization of actin on tachyzoites. Our findings add complexity to the study of the apicomplexan actin in cellular processes, since the multiple functions of this important protein might be regulated by mechanisms involving post-translational modifications.
Evaluation of the Efficacy of Combined Therapy of Methotrexate and Etanercept versus Methotrexate as a Mono-Therapy

AIM: This study aims to evaluate the efficacy of Methotrexate (MTX) alone and combined therapy with Etanercept (ETN) and Methotrexate in patients with active rheumatoid arthritis (RA). METHODS: In the randomised control study, conducted in the period from March 2014 until March 2016, we evaluated the efficacy of the treatment of patients with RA with MTX as monotherapy and combination treatment with MTX and ETN. In the Clinic of Rheumatology in Prishtina, 90 adult patients with RA were treated in combination with ETN (doses of 50 mg subcutaneously/weekly), with oral MTX (doses up to 20 mg weekly), and MTX alone (doses up to 20 mg weekly) during this period of two years. Clinical response was assessed using European League against Rheumatism (EULAR)/American College of Rheumatology (ACR) Criteria and the Disease Activity Score (DAS28). Radiographic changes were measured in the beginning and at the end of the study using Larsen’s method. RESULTS: Of the cohort groups of 90 patients, mean age of 55.63, 15 patients, (16.6 %) were treated with combined therapy (ETN plus MTX) and 75 patients (83.3%) with monotherapy (MTX). After two years of treatment the group with combined therapy resulted with improvement of acute phase reactants as erythrocyte sedimentation rate (ESR) for the first hour (41.1 vs. 10.3 mm/hour) and C - reactive protein (CRP) (40.8 vs. 6 mg/liter), and compared to the group treated with monotherapy, there were no significant changes (ESR: 45.7 vs 34.3 mm/hour; CRP: 48 vs 24 mg/liter). Before the treatment, the severity of the disease was high, wherein the group with combined therapy DAS28 was 5.32, compared to the monotherapy group whom DAS28 was 5.90. After 2 years of treatment, we had significant changes in the results of DAS28, wherein the group treated with ETN plus MTX DAS28 was 2.12 ± 0.15, while in the group of patients treated with MTX DAS28 were 3.75 ± 0.39 (t = 13.03; df = 58; p < 0.0001). The group with combined therapy showed no evidence of radiographic progression comparing to the group of patients with monotherapy. CONCLUSIONS: Based on our results achieved during 2 years we can conclude that ETN in combination with MTX reduced disease activity, slowed radiographic progression and improved clinical manifestations more effectively than MTX alone. No serious adverse events were noticed in the group with combination treatment.
Influence of Data Analysis, Entrepreneurial and Business Skills on Information Technology Firms: A Dynamic Capabilities Approach

In the contemporary business environment, the innovation and quality of the Information Technology (IT) industry’s products and services depend to a grea t extent on the knowledge, ability and talent applied by IT professionals. IT professionals’ skills and capacities to integrate, build, and reconfigure resources in the continuously evolving business environments is critical. In particular, inefficiencies usually come from the lack of skills or IT professionals’ inability to apply them in a way that allows a firm to adapt and evolve concurrently with business demands. With these challenges in mind, this paper presents a study conducted with 72 IT professionals to test the importance that data analytics, entrepreneurial, and business skills have in enhancing employees’ dynamic capabilities and ultimately their perceived work performance. This study confirms that there is an important association between the three types of skills and employees’ ability to effectuate action that helps a firm evolve. By fostering such a capacity which is critical in modern day firms, their perception about their work performance is ultimately affected.
Ultrasonography-guided Cannula Method for Hyaluronic Acid Filler Injection with Evaluation using Laser Speckle Flowgraphy

Summary: Hyaluronic acid (HA) is present in the connective tissues of the skin and decreases with age. HA fillers are popular as facial rejuvenation treatments. They are generally considered safe; however, complications, such as cutaneous necrosis and blindness due to vascular embolism, sometimes occur. Because vascular embolisms are likely associated with the deep placement of HA fillers, a strategy that involves injection into superficial regions (the conventional method) is commonly used to reduce risks. However, deep injections to achieve revolumization are becoming common, even in high-risk areas for intravascular infusion. We aimed to study the usefulness of the ultrasonography-guided cannula method for preventing intravascular infusion of HA fillers. An HA filler was injected into the region just under the dermis on the left side of the face of a 38-year-old man using the conventional method, and another HA filler was injected into the periosteum on the right side using the ultrasonography-guided cannula method. The skin blood flow on both sides was compared using laser speckle flowgraphy (LSFG). The ultrasonography-guided method was successful in detecting the cannula and the blood vessel, and the HA filler was safely injected into a deep region. Using LSFG, a difference in skin blood flow between the 2 methods was detected. The ultrasonography-guided cannula method was effective in aiding the safe injection of an HA filler in a deep high-risk area and maintained skin blood flow. LSFG may be adopted to evaluate skin blood flow after HA filler injections.
Representation Effects and Loss Aversion in Analytical Behaviour: An Experimental Study into Decision Making Facilitated by Visual Analytics

This paper presents the results of an experiment into the relationship between the representation of data and decision-making. Three hundred participants online, were asked to choose between a series of financial investment opportunities using data presented in line charts. A single dependent variable of investment choice was examined over four levels of varying display conditions and randomised data. Three variations to line chart visualisations provided a controlled factor between subjects divided into three groups; -˜standard’ line charts, -˜tall’ line charts, and one dual-series line chart. The final results revealed a consistent main effect and two other interactions between certain display conditions and decision-making. The findings of this paper are significant to the study visualisation and to the field of visual analytics. This experiment was devised as part of a study into Analytical Behaviour, defined as decision-making facilitated by visual analytics - a new topic that encompasses existing research and real-world applications.
A Variational Recurrent Neural Network for Session-Based Recommendations using Bayesian Personalized Ranking

This work introduces VRNN-BPR, a novel deep learning model, which is utilized in session-based Recommender systems tackling the data sparsity problem. The proposed model combines a Recurrent Neural Network with an amortized variational inference setup (AVI) and a Bayesian Personalized Ranking in order to produce predictions on sequence-based data and generate recommendations. The model is assessed using a large real-world dataset and the results demonstrate its superiority over current state-of-the-art techniques.
StarGAN-VC: non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks

This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN. Our method, which we call StarGAN-VC, is noteworthy in that it (1) requires no parallel utterances, transcriptions, or time alignment procedures for speech generator training, (2) simultaneously learns many-to-many mappings across different attribute domains using a single generator network, (3) is able to generate converted speech signals quickly enough to allow real-time implementations and (4) requires only several minutes of training examples to generate reasonably realistic sounding speech. Subjective evaluation experiments on a non-parallel many-to-many speaker identity conversion task revealed that the proposed method obtained higher sound quality and speaker similarity than a state-of-the-art method based on variational autoencoding GANs.
Neuropsychological changes in a prospectively followed cohort of intravenous drug users with and without HIV

We followed a cohort of 223 intravenous drug users (99 HIV - and 124 HIV + ) for up to 3.5 years, examining change in performance over time as a function of HIV status, disease severity, and neurological signs and symptoms. Analyses were performed by applying generalized estimating equations (GEE) to regression analyses with repeated measures, and controlled for age, education, and length of substance use. None ofthe subjects had AIDS at baseline. There were 147 men (85 HIV + and 62 HIV - ) and 76 women (39 HIV + and 37 HIV - ). Memory performance was worse in the HIV + than HIV - women. In the men, performance on the memory, executive, language, and attention factors improved significantly over time, but this improvement was attenuated in the HIV + men for the attention and orientation factors. In the HIV + women, AIDS was associated with worsening performance on attention tests. The presence or onset of clinically significant neurological findings was associated with poorer language and motor speed performance. In the HIV + men, memory performance was worse when the CD4 count fell below 200; it declined over time in men with AIDS but not in those without. A learning effect for language was attenuated in men who developed AIDS. The presence or development ofa clinically significant neurological sign was associated with poorer memory, executive, language, attention, and motor speed performance. Our findings parallel those that we previously reported in a prospectively followed cohort of gay men. In combination, our studies of gay men and IDU cohorts suggest that (a) HIV can affect cognition early, even when the patient is medically asymptomatic; (b) cognitive difficulties worsen as the severity of HIV infection increases; and (c) the advent of clinically significant neurologic signs is associated with progression to more severe cognitive deficits. Our data suggest that the neurological and neuropsychological changes are both manifestations of the central effect of HIV on the CNS.
SHARPP Games for the Education Prevention and Reversion of Chronic Diseases

Prevalence of chronic diseases has become a serious problem in our society. These illnesses are commonly caused by dietary and lifestyle risk factors and are hard to cure completely. However, behavioral changes such as adopting a healthy diet and lifestyle are effective in reducing chronic conditions. In this research, we propose to build games that could support the Sustainable transformation of chronic patients in a Holistic and Adaptable manner using Real-time, Precise, and Persuasive (SHARPP) principles, processes, systems, and technologies. This research leverages wearable information technologies and chronic disease studies to design games that interweaves virtual worlds with the real world. It not only supports patients to form healthy habits that prevent and revert chronic diseases but also helps individuals to balance various life dimensions.
PPARgamma-mediated ALDH1A3 suppression exerts anti-proliferative effects in lung cancer by inducing lipid peroxidation

Abstract Context: The metabolic function of peroxisome proliferator-activated receptor gamma (PPARγ) in lung cancer remains unclear. Objectives: To determine the relationship of PPARγ on ALDH1A3-induced lipid peroxidation to inhibit lung cancer cell growth. Materials and methods: In silico analysis using microarray dataset was performed to screen the positive correlation between PPARγ and all ALDH isoforms. NUBIscan software and ChIP assay were used to identify the binding sites (BSs) of PPARγ on ALDH1A3 promoter. The expression of ALDH1A3 under thiazolidinedione (TZD) treatment was evaluated by QPCR and Western Blot in HBEC and H1993 cell lines. Upon treatment of TZD, colony formation assay was used to check cell growth inhibition and 4-hydroxy-2-nonenal (4HNE) production as lipid peroxidation marker was determined by Western Blot in PPARγ positive cell H1993 and PPARγ negative cell H1299. Results: Compared to other ALDH isoforms, ALDH1A3 showed the highest positive correlation to PPARγ expression. ALDH1A3 upregulated PPARγ expression while PPARγ activation suppressed ALDH1A3. Among 2 potential screened PPARγ response elements, BS 1 and 2 in the promoter of ALDH1A3 gene, PPARγ bound directly to BS2. Ligand activation of PPARγ suppressed mRNA and protein expression of ALDH1A3. Growth inhibition was observed in H1993 (PPARγ positive cell) treated with PPARγ activator and ALDH inhibitor compared to H1299 (PPARγ negative cell). PPARγ activation increased 4HNE which is known to be suppressed by ALDH1A3. Conclusions: ALDH1A3 suppression could be one of PPARγ tumor suppressive function. This study provides a better understanding of the role of PPARγ in lung cancer.
Molecular and ecological signatures of an expanding hybrid zone

Abstract Many species are currently changing their distributions and subsequently form sympatric zones with hybridization between formerly allopatric species as one possible consequence. The damselfly Ischnura elegans has recently expanded south into the range of its ecologically and morphologically similar sister species Ischnura graellsii. Molecular work shows ongoing introgression between these species, but the extent to which this species mixing is modulated by ecological niche use is not known. Here, we (1) conduct a detailed population genetic analysis based on molecular markers and (2) model the ecological niche use of both species in allopatric and sympatric regions. Population genetic analyses showed chronic introgression between I. elegans and I. graellsii across a wide part of Spain, and admixture analysis corroborated this, showing that the majority of I. elegans from the sympatric zone could not be assigned to either the I. elegans or I. graellsii species cluster. Niche modeling demonstrated that I. elegans has modified its environmental niche following hybridization and genetic introgression with I. graellsii, making niche space of introgressed I. elegans populations more similar to I. graellsii. Taken together, this corroborates the view that adaptive introgression has moved genes from I. graellsii into I. elegans and that this process is enabling Spanish I. elegans to occupy a novel niche, further facilitating its expansion. Our results add to the growing evidence that hybridization can play an important and creative role in the adaptive evolution of animals.
Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification

The wide usage of Machine Learning (ML) has lead to research on the attack vectors and vulnerability of these systems. The defenses in this area are however still an open problem, and often lead to an arms race. We define a naive, secure classifier at test time and show that a Gaussian Process (GP) is an instance of this classifier given two assumptions: one concerns the distances in the training data, the other rejection at test time. Using these assumptions, we are able to show that a classifier is either secure, or generalizes and thus learns. Our analysis also points towards another factor influencing robustness, the curvature of the classifier. This connection is not unknown for linear models, but GP offer an ideal framework to study this relationship for nonlinear classifiers. We evaluate on five security and two computer vision datasets applying test and training time attacks and membership inference. We show that we only change which attacks are needed to succeed, instead of alleviating the threat. Only for membership inference, there is a setting in which attacks are unsuccessful (<10% increase in accuracy over random guess). Given these results, we define a classification scheme based on voting, ParGP. This allows us to decide how many points vote and how large the agreement on a class has to be. This ensures a classification output only in cases when there is evidence for a decision, where evidence is parametrized. We evaluate this scheme and obtain promising results.
An Intelligent Decision Support System for the Empty Unit Load Device Repositioning Problem in Air Cargo Industry

Unit load devices (ULDs) are containers and pallets used in the air cargo industry to bundle freight for efficient loading and transportation. Mainly due to imbalances in global air transportation networks, deficits and surpluses of ULDs are the result and require stock balancing through the repositioning of (empty) ULDs. Following a design science research approach, we (1) elaborate the hitherto uninvestigated problem class of empty ULD repositioning (EUR) and (2) propose an intelligent decision support system (IDSS) that incorporates a heuristic for the given problem and combines artificial intelligence (i.e., rule-based expert system technology) with business analytics. We evaluate the IDSS with real-world data and demonstrate that the proposed solution is both effective and efficient. In addition, our results provide empirical evidence regarding the positive economic and ecological impact of leveraging the potential of ULD pooling in multi-carrier networks.
A connected character recogniser using level building of HMMs

The recognition of a printed word is traditionally limited by its segmentation into individual characters due to the noise introduced by facsimile transmission, photocopying, handling or ageing. In this paper, a character recogniser is described which does not require a prior segmentation of the word, and is thus suited to the recognition of noisy text images. A novel method is described whereby hidden Markov models are used to recognise a horizontal "profile" of a word generated from an analysis of each vertical line of pixels in its image.
Routes to Open-Endedness in Evolutionary Systems

This paper presents a high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems. Drawing upon work by Banzhaf et al., three different kinds of open-endedness are identified: exploratory, expansive, and transformational. These are characterised in terms of their relationship to the search space of phenotypic behaviours. A formalism is introduced to describe three key processes required for an evolutionary process: the generation of a phenotype from a genetic description, the evaluation of that phenotype, and the reproduction with variation of individuals according to their evaluation. The formalism makes explicit various influences in each of these processes that can easily be overlooked. The distinction is made between intrinsic and extrinsic implementations of these processes. A discussion then investigates how various interactions between these processes, and their modes of implementation, can lead to open-endedness. However, it is demonstrated that these considerations relate to exploratory open-endedness only. Conditions for the implementation of the more interesting kinds of open-endedness - expansive and transformational - are also discussed, emphasizing factors such as multiple domains of behaviour, transdomain bridges, and non-additive compositional systems. In contrast to a traditional population genetics analysis, these factors relate not to the generic evolutionary properties of individuals and populations, but rather to the nature of the building blocks out of which individual organisms are constructed, and the laws and properties of the environment in which they exist. The paper ends with suggestions of how the framework can be used to categorise and compare the open-ended evolutionary potential of different systems, and how it might guide the design of systems with greater capacity for open-ended evolution.
Learning an Approximate Model Predictive Controller With Guarantees

A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction. The framework can be used for a wide class of nonlinear systems. Any standard supervised learning technique (e.g., neural networks) can be employed to approximate the MPC from samples. In order to obtain closed-loop guarantees for the learned MPC, a robust MPC design is combined with statistical learning bounds. The MPC design ensures robustness to inaccurate inputs within given bounds, and Hoeffding’s Inequality is used to validate that the learned MPC satisfies these bounds with high confidence. The result is a closed-loop statistical guarantee on stability and constraint satisfaction for the learned MPC. The proposed learning-based MPC framework is illustrated on a nonlinear benchmark problem, for which we learn a neural network controller with guarantees.
Research Overview of Clock Synchronization in Wireless Sensor Networkn

Clock synchronization is the key technology of Wireless Sensor Networks, since inaccurate clocks have a significant impact on network accuracy and security. In this paper, an important application of clock synchronization in wireless sensor networks is introduced. It is concluded that the deterministic causes of clock out-ofsynchronization are the effects of offset and drift rate. Five classic methods of improved synchronization method for comparative study. Including: the improved method of least squares ,delay measurement time synchronization, sensor network timing synchronization ,tiny synchronization, lightweight tree synchronization. And we summed up non deterministic factors such as precision, complexity, overhead and so on are summarized, and then we put forward the corresponding problems. Finally, the new development direction and trend of wireless sensor time synchronization algorithm are proposed.