Miracle-dz/UniFAR-FTUs huggingface dataset:
In vitro and in vivo models for peritonitis demonstrate unchanged neutrophil migration after exposure to dialysis fluids.

BACKGROUND Recurrent infections in peritoneal dialysis (PD) patients may alter the abdominal wall resulting in an impairment of its dialysis capacity. In this study we investigated both in vitro and in vivo the effects of mesothelial exposure to dialysis fluids on the migration of neutrophils and their capacity to clear a bacterial infection. METHODS First, we evaluated neutrophil migration in an in vitro transwell model for the peritoneal membrane with monolayers of primary human mesothelial cells (MC) on the lower side and primary human endothelial cells (EC) on top of the same transwell membrane, upon exposure of MC to PD fluid (PDF)-derived components. In addition to this in vitro model, we combined chronic peritoneal exposure to PDF with a peritoneal infection model in the rat. We investigated the kinetics of the chemokine response, neutrophil recruitment and bacterial clearance. RESULTS Known chemoattractants, such as fMLP and IL-8, strongly increased neutrophil migration across both cell layers in the in vitro model of the peritoneal membrane. Pre-incubation of the MC layer for 48 h with 55 mM glucose, a combination of two glucose degradation products, methylglyoxal and 3-deoxyglucosone, or conventional dialysis fluid (1:4 dilution), however, did not change the IL-8-induced migration of neutrophils. In concert with this finding we demonstrated an unchanged MC expression of ICAM-1 and VCAM-1 after these pre-treatments. Unexpectedly, chronic i.p. exposure to conventional PDF or a recently developed lactate/bicarbonate-buffered PDF in a rat peritoneal exposure model strongly hampered the chemokine response upon bacterial challenge. Nevertheless, neutrophil recruitment and bacterial clearance were effective and did not differ from rats not pre-exposed to PDF. CONCLUSIONS We conclude that exposure of MC to PDF does not hamper the recruitment of functional neutrophils upon challenge.
Spatial and Temporal Word Spectrum of Social Media

We assume that different users from different geolocations will post different microblog messages for their local issues on social networks. Based on this assumption, we calculate unique features of social media for different geolocations in different time periods. The outcome of this calculation is called a SpatialTemporal Word Spectrum (STWS) model which is a linguistic fingerprint of a geolocation on social media. We use STWS as a baseline to catch the prominent and statistical features of microblogs as a spectral representation of the words used by social network users. As a baseline of the social media, STWS can be used to detect emerging local events. It can also be used to guess the location of a user if her/his location is unknown and the posted microblog exhibits the spectral features of that location. We show how STWS is visualized and how it is used to reveal behavioral features of local social network users. Our experiments show that the proposed method is effective and STWS opens a new way of studying social media.
Robust low altitude behavior control of a quadrotor rotorcraft through sliding modes

This paper gives the full dynamical model of a commercially available quadrotor rotorcraft and presents its behavior control at low altitudes through sliding mode control. The control law is very well known for its robustness against disturbances and invariance during the sliding regime. The plant, on the other hand, is a nonlinear one with state variables are tightly coupled. The simulations have shown that the algorithm successfully drives the system towards the desired trajectory with bounded control signals.
Sub-structure-based estimation of pronunciation proficiency and classification of learners

Automatic estimation of pronunciation proficiency has its specific difficulty. Adequacy in controlling the vocal organs can be estimated from spectral envelopes of input utterances but the envelope patterns are also affected easily by different speakers. To develop a pedagogically sound method for automatic estimation, the envelope changes caused by linguistic factors and those by extra-linguistic factors should be properly separated. For this aim, in our previous study [1], we proposed a mathematically-guaranteed and linguistically-valid speaker-invariant representation of pronunciation, called speech structure. After the proposal, we have examined that representation also for ASR [2], [3], [4] and, through these works, we have learned better how to apply speech structures to various tasks. In this paper, we focus on a proficiency estimation experiment done in [1] and, based on our recently proposed techniques for the structures, we carry out that experiment again but under new and different conditions. Here, we use smaller units of structural analysis, speaker-invariant substructures, and relative structural distances between a learner and a teacher. Results show that correlations between human and machine rating are improved and also show extremely higher robustness to speaker differences compared to widely used GOP scores. Further, we also demonstrate that the proposed representation can classify learners purely based on their pronunciation proficiency, not affected by their age and gender.
Novel approach for satellite image resolution and contrast enhancement using wavelet transform and Brightness Preserving Dynamic Histogram Equalization

A new satellite image resolution and contrast enhancement technique based on DWT (Discrete wavelet transform), Stationary Wavelet Transform (SWT) and BPDHE (Brightness Preserving Dynamic Histogram Equalization) has been proposed. The wavelet transform is used to construct the high resolution image. The BPDHE is used to enhance the contrast of the image. The low resolution low contrast image is decomposed by using Discrete Wavelet Transform and Stationary Wavelet Transform. The subbands are interpolated by Lanczos interpolation. The intermediate process is carried out to produce low contrast high resolution image. The contrast is enhanced by using Brightness Preserving Dynamic Histogram Equalization. The Output of BPDHE is decomposed by Wavelet Transform and the new LL is constructed using Singular Value Decomposition. The subbands are recombined using inverse wavelet transform. The proposed method enhances the resolution and contrast of the low resolution and contrast image. The quantitative metrics are measured to show that the proposed method is superior to the conventional method.
A mixture of experts model for rank data with applications in election studies

A voting bloc is defined to be a group of voters who have similar voting preferences. The cleavage of the Irish electorate into voting blocs is of interest. Irish elections employ a "single transferable vote" electoral system; under this system voters rank some or all of the electoral candidates in order of preference. These rank votes provide a rich source of preference information from which inferences about the composition of the electorate may be drawn. Additionally, the influence of social factors or covariates on the electorate composition is of interest. A mixture of experts model is a mixture model in which the model parameters are functions of covariates. A mixture of experts model for rank data is developed to provide a model-based method to cluster Irish voters into voting blocs, to examine the influence of social factors on this clustering and to examine the characteristic preferences of the voting blocs. The Benter model for rank data is employed as the family of component densities within the mixture of experts model; generalized linear model theory is employed to model the influence of covariates on the mixing proportions. Model fitting is achieved via a hybrid of the EM and MM algorithms. An example of the methodology is illustrated by examining an Irish presidential election. The existence of voting blocs in the electorate is established and it is determined that age and government satisfaction levels are important factors in influencing voting in this election.
Inhibition of p-IκBα Ubiquitylation by Autophagy-Related Gene 7 to Regulate Inflammatory Responses to Bacterial Infection.

BACKGROUND Klebsiella pneumoniae causes serious infections and healthcare burdens in humans. We have previously reported that the deficiency of autophagy-related gene (Atg) 7 in macrophages (murine alveolar macrophage cell line [MH-S]) induced irregular host immunity against K. pneumoniae and worsened pathologic effects in the lung. In the current study, we investigated the molecular mechanism by which Atg7 influenced K. pneumoniae-induced inflammatory responses. METHODS Expression levels of Atg7, ubiquitin (Ub), and tumor necrosis factor (TNF) α and phosphorylation of IκBα (p-IκBα) were determined with immunoblotting. Ubiquitylation of p-IκBα was determined with immunoprecipitation. RESULTS We noted an interaction between Atg7 and p-IκBα, which was decreased in MH-S after K. pneumoniae infection, whereas the interaction between Ub and p-IκBα was increased. Knock-down of Atg7 with small interfering RNA increased p-IκBα ubiquitylation, promoted nuclear factor κB translocation into the nucleus, and increased the production of TNF-α. Moreover, knock-down of Ub with lentivirus-short hairpin RNA Ub particles decreased binding of p-IκBα to Ub and inhibited TNF-α expression in the primary alveolar macrophages and lung tissue of atg7-knockout mice on K. pneumoniae infection. CONCLUSIONS Loss of Atg7 switched binding of p-IκBα from Atg7 to Ub, resulting in increased ubiquitylation of p-IκBα and intensified inflammatory responses against K. pneumoniae. Our findings not only reveal a regulatory role of Atg7 in ubiquitylation of p-IκBα but also indicate potential therapeutic targets for K. pneumoniae control.
Image and tag retrieval by leveraging image-group links with multi-domain graph embedding

A large number of images are available on online photo-sharing services along with rich meta-data, including tags, groups, and locations, etc. For associating two domains of different modalities, e.g. images and tags, Canonical Correlation Analysis (CCA) and its extended methods are used widely. We employ a more flexible graph embedding method called Cross-Domain Matching Correlation Analysis (CDMCA), which can deal with many-to-many relationships between any number of domains, for associating images, tags, and groups. Experiments of Tag-to-Image and Image-to-Tag retrieval tasks are performed on Flickr images. Tags are represented by feature vectors based on semantic word embedding, which enables the searching of even unseen tags. Groups are collections of images, and they provide the information of image-group links. Our experiments show that image and tag retrieval tasks improve by utilizing the group information as the third domain.
The fault diagnosis for centrifugal compressor based on time series analysis with neutral network

In order to have fault diagnosis for centrifugal compressor correctly, the times series analysis with neural network was carried out for it. The theory model of time series analysis method was put forward firstly, and the autoregressive coefficients diagnosis method was discussed then. And the neural network diagnosis method with AR coefficients was put forward at last. And the fault diagnosis results of centrifugal compressor were acquired based on BPNN and distance diagnosis method, and diagnosis results was good.
Towards a Conversational Corpus for Human-Robot Conversations

Conversational corpora based on human-human dialogues have often been used for training of data-driven dialogue systems. However, human-human conversations might not be the optimal inputs for machine learning training aims used in HRI. This paper suggests the creation of a conversational corpus based on Human-Robot conversations as input for the training of a dialogue system used in future conversational robots. We propose that the significant differences between Human-Human Conversation (HHC) and Human-Robot Conversation (HRC) in terms of used language and other aspects (e.g., humanlikeness, embodiment, etc.) might affect the quality of the responses from a conversational robot. Hence, the use of HRCs as an input could improve the responses of the robots when the conversational machine learning system is trained using a more realistic model of HRI conversations rather than a HHI model. Future applications of conversational robots in education and health care could be enhanced by using an appropriate HRC corpus.
Escaping from a Liquidity Trap and Deflation: The Foolproof Way and Others

Existing proposals to escape from a liquidity trap and deflation, including my Foolproof Way,' are discussed in the light of the optimal way to escape. The optimal way involves three elements: (1) an explicit central-bank commitment to a higher future price level; (2) a concrete action that demonstrates the central bank's commitment, induces expectations of a higher future price level and jump-starts the economy; and (3) an exit strategy that specifies when and how to get back to normal. A currency depreciation is a direct consequence of expectations of a higher future price level and hence an excellent indicator of those expectations. Furthermore, an intentional currency depreciation and a crawling peg, as in the Foolproof Way, can implement the optimal way and, in particular, induce the desired expectations of a higher future price level. I conclude that the Foolproof Way is likely to work well for Japan, which is in a liquidity trap now, as well as for the euro area and the United States, in case either would fall into a liquidity trap in the future.
Spotting Knowledge Base Facts in Web Texts

Knowledge bases (KB) such as DBpedia, YAGO and Freebase have been constructed by harvesting facts from high-quality data sources and incorporating community contributions. Accurately detecting occurrences of these KB facts in complementary sources (sources other than where they were extracted from) is crucial for fact validity assessments and deriving occurrence statistics. In this paper we consider fact spotting – the task of automatically discovering the mentions of KB facts in text documents. Our fact spotting methodology follows a two-stage approach. First, we perform similarity-based matching of noun phrases with labels of KB entities and dependency path structures with patterns of KB relations. Next, we perform joint matching of mentions, entities, paths, relations, and their textual locations by encoding them into variables of an integer linear program. We evaluate our method by spotting Freebase facts in biographies on the Web.
Towards a Methodology Employing Critical Parameters to Deliver Performance Improvements in Interactive Systems

In the design of interactive systems, overall performance issues tend to receive less attention than provision of functionality. As a result, systems may not always offer perform- ance improvements to their users. In this paper we present some proposals for methods based on the use of application-specific critical parameters, i.e., performance parameters that measure how well the system serves its purpose. We argue that these parameters can provide a basis for dealing with performance issues in design. To construct this basis, there is a need to focus on identifying parameters through field studies, and on constructing models for use in making performance pre- dictions. We provide a number of examples of critical parameters, and discuss problems we have encountered in identifying them and in model construction. We summarise the results of a study of designers working with critical parameters. A final section of the paper discusses the feasibility of introducing critical parameters to HCI design practice.
Representing Clinical Knowledge in Oral Medicine Using Ontologies

How can information technology be used to model and handle clinical knowledge in everyday work so that clinicians can more systematically learn from collected clinical data? Ontology is a crucial element in answering this question. Based on nearly ten years of clinical experience, fundamental requirements of an ontology for oral medicine are presented. The use of the proposed W3C standards RDF and OWL for the design and implementation of an ontology for oral medicine is then described and discussed. The reported work contributes to knowledge representation in oral medicine by presenting the pioneering work towards an ontology for oral medicine using RDF/OWL, thereby testing the latter on a new domain.
Creating Zombilingo, a game with a purpose for dependency syntax annotation

This paper presents the design of Zombilingo, a Game With A Purpose (GWAP) that allows for the dependency syntax annotation of French corpora. The development will start mid-2014 and the game is to be made available by the end of the year. The created language resource will be freely and continuously available on the game Web site.
The Worldwide Expansion of Early Childhood Care and Education, 1985–2010

Although early childhood care and education (ECCE) enrollments have expanded significantly around the world since the 1960s, little is known about the economic, social, and cultural drivers explaining this growth. Using country fixed effects and a sample of 117 countries, this study explores three predictors framing the growth of ECCE enrollments across countries and over time: economic development, improvements in women’s status, and country linkages to world society. The findings suggest that economic development was most strongly related to growth in ECCE enrollment before 1995 and that women’s status and world society indicators were even stronger predictors between 2000 and 2010. Analyses by country type suggest differences for developing countries and countries in transition compared with developed countries.
Serum Response Factor Is Required for Cortical Axon Growth But Is Dispensable for Neurogenesis and Neocortical Lamination

Previous studies have shown that neuron-specific deletion of serum response factor (SRF) results in deficits in tangential cell migration, guidance-dependent circuit assembly, activity-dependent gene expression, and synaptic plasticity in the hippocampus. Furthermore, SRF deletion in mouse embryonic stem cells causes cell death in vitro. However, the requirement of SRF for early neuronal development including neural stem cell homeostasis, neurogenesis, and axonal innervations remains unknown. Here, we report that SRF is critical for development of major axonal tracts in the forebrain. Conditional mutant mice lacking SRF in neural progenitor cells (Srf-Nestin-cKO) exhibit striking deficits in cortical axonal projections including corticostriatal, corticospinal, and corticothalamic tracts, and they show a variable loss of the corpus callosum. Neurogenesis and interneuron specification occur normally in the absence of SRF and the deficits in axonal projections were not due to a decrease or loss in cell numbers. Radial migration of neurons and neocortical lamination were also not affected. No aberrant cell death was observed during development, whereas there was an increase in the number of proliferative cells in the ventricular zone from embryonic day 14 to day 18. Similar axonal tract deficits were also observed in mutant mice lacking SRF in the developing excitatory neurons of neocortex and hippocampus (Srf-NEX-cKO). Together, these findings suggest distinct roles for SRF during neuronal development; SRF is specifically required in a cell-autonomous manner for axonal tract development but is dispensable for cell survival, neurogenesis, neocortical lamination, and neuronal differentiation.
Magnetism, rotation and accretion in Herbig Ae-Be stars

Abstract Studies of stellar magnetism at the pre-main sequence phase can provide important new insights into the detailed physics of the late stages of star formation, and into the observed properties of main sequence stars. This is especially true at intermediate stellar masses, where magnetic fields are strong and globally organised, and therefore most amenable to direct study. This talk reviews recent high-precision ESPaDOnS observations of pre-main sequence Herbig Ae-Be stars, which are yielding qualitatively new information about intermediate-mass stars: the origin and evolution of their magnetic fields, the role of magnetic fields in generating their spectroscopic activity and in mediating accretion in their late formative stages, and the factors influencing their rotational angular momentum.
Hybrid iSLIP scheduling algorithm for Network on Chip

Network on Chip is a set of hardware component that communicates together. The communication system must achieve maximum throughput while maintaining the system correctness, stability and eliminating starvation. The ideal solution among existing NoC architecture is the Switch Fabric. As it is known, the scheduler is the Switch Fabric brain. Moreover, the scheduling algorithm choice must be suitable to the application requirement. By nature, NoC architecture is composed of two types of core that are master cores and slave ones, called also processor and coprocessor. Trying to give the suitable solution for such hardware architecture, we propose our own scheduling algorithm called Hybrid iSLIP which is a kind of prioritized iSLIP. This paper presents a modeling, design and an evaluation of a hardware scheduler for High-Speed Virtual output Queuing using the Hybrid iSLIP algorithm. A comparison is done between the iSLIP algorithm and the Hybrid one by determining the complexity and the convergence order for both them.
A Rate Adaptive Bit-loading Algorithm for a Discrete Multi-tone Modulation System in Downstream Power Line Communications

This paper deals with a variable rate Discrete Multitone Modulation (DMT) communication system for broadband Power-Line Communications (PLCs), based on the bit-loading algorithm proposed by Leke and Cioffi. In the proposed system a proper LMS channel estimator will be considered, which is based on the insertion of a Training Sequence (TS). The proposed approach will be compared with ideal channel estimation case, so showing its effectiveness. Moreover, different TS lengths will be compared. The performance of the system, expressed in terms of Bit-Rate and Bit Error Rate (BER), with and without an estimation error is derived by simulation under the assumption of frequency-selective multipath fading channel and additive colored Gaussian noise, according to the in-building networks model.
BaaS-4US: A Framework to Develop Standard Backends as a Service for Ubiquitous Applications

Internet of Things (IoT) is presented as the next computing and interaction paradigm to be implemented in society, after current advances in Web, mobile and cloud-based technologies. However, there are still a large number of open challenges to be addressed for its successful deployment. Many proposals are focused on hardware and low-level technical issues, but the use of software design based techniques/methods can also provide significant advantages in terms of an easier development of IoT applications. This research work introduces a framework intended to facilitate the development of backend service platforms based on standards. It follows the ideas behind the Internet of Services (IoS) paradigm and cloud computing. And the main purpose of this contribution is to provide a Web Services deployment via their behaviour specification which is independent of actual and future technologies / standards.