Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of Hospitalized Children with 2009 Pandemic Influenza A (H1N1): A Multicenter Study in Korea

The majority of Korean patients with pandemic influenza A (H1N1) during the 2009 epidemic were under 20 yr of age. The limited data on the clinical characteristics of these children led us to conduct a case note-based investigation of children admitted to 6 university hospitals with 2009 H1N1 influenza. A total of 804 children was enrolled. The median age was 5 yr; 63.8% were males; and 22.4% had at least one chronic underlying disease. Ninety-five of the patients (11.8%) were critically ill and they suffered more from shortness of breath, dyspnea and lymphopenia than the other patients. Among all the patients, 98.8% were treated with antivirals and 73% received treatment within 48 hr of illness onset. All the enrolled patients are alive and appear to have had good outcomes, probably due to the early intervention and antiviral treatment. This study deals with hospitalized children whose diagnoses of influenza A (H1N1) were confirmed, and therefore provides important new information about the clinical patterns of children with influenza A (H1N1) in Korea.
Relationship of CK8/18 expression pattern to breast cancer immunohistochemical subtyping in Egyptian patients

The immunohistochemical (IHC) subtyping of breast cancer can be a useful substitute for gene expression analysis. The aim of this study was to investigate the relationship of CK8/18 to the biology of breast carcinoma (BC) represented by its IHC subtypes. The IHC expression of CK8/18 was correlated with IHC subtypes of BC using ER, PR, HER2/neu, and Ki67 LI (with cutoff 14%). All cases showed CK 8/18 expression in tumour cells with varying degree of intensities; 49/70 cases (70%) showed diffuse cytoplasmic expression (loss of membranous pattern), while 21/70 cases (30%) showed membrano-cytoplasmic pattern. Adjacent non-neoplastic breast lobules showed membrano-cytoplasmic pattern in 58% of cases, which was significantly different from the pattern in invasive cancer (P = 0.002). A loss of membranous pattern in malignant tumours was significantly associated with higher tumour grade (P = 0.02), higher mitotic count (P = 0.03), and negative HER2/neu status (P = 0.04). CK 8/18 H score ranged between 1 and 290 with mean ± SD was 181 ± 70.54. Tumours with lower CK 8/18 H score were in the advanced stage group (P = 0.04). Low CK8/18 H score and loss of membranous pattern were significantly associated with triple negative (TN) subtype as compared with luminal subtype (P = 0.006 and P = 0.026, respectively). In addition, CK8/18 with lost membranous pattern was significantly associated with TN subtype compared with HER2/neu positive subtype (P = 0.001). However, there was no significant difference between luminal A and B subtypes regarding CK8/18 H score or pattern of expression. This study concluded that low CK8/18 H score and loss of membranous pattern of CK8/18 are associated with worse prognostic features and TN subtype.
Long-term benzodiazepine use in patients taking antidepressants in a public health setting in Brazil: a cross-sectional study

Objective The aims of the study were to investigate the prevalence of long-term use of benzodiazepines in patients taking antidepressants and to identify the risk factors associated with the prolonged use of benzodiazepines. Design Cross-sectional study. Setting Public health system in Brazil. Participants Outpatients using antidepressants from January 2008 to December 2009 were included. The data were obtained from pharmacy databases and medical records. All individuals in the database were included in the study and were classified into two categories: (1) patients who had not used benzodiazepines combined with antidepressants or had combined the use of antidepressants with benzodiazepines for a short period (up to 4 weeks), and (2) those who used antidepressants plus benzodiazepines for a longer period (more than 4 weeks). Main outcome measure The outcome measure is prolonged use of benzodiazepines (more than 4 weeks). We conducted a multivariate analysis to identify the factors associated with prolonged use of benzodiazepines. Results Forty per cent of the 870 patients evaluated had prolonged use of benzodiazepines (more than 4 weeks). The risk factors associated with prolonged use were age above 35 years (prevalence ratio (PR): 2.18, 95% CI 1.55 to 3.06, P<0.001), female sex (PR: 1.47, 95% CI 1.07 to 2.02, P=0.019), diagnosis at least 3 years prior (PR: 2.1, 95% CI 1.6 to 2.8, P<0.001), use of selective serotonin reuptake inhibitor antidepressants (PR: 1.7, 95% CI 1.3 to 2.2, P<0.001) and having a prescription from a psychiatrist (PR: 6.5, 95% CI 3.2 to 13.2, P<0.001). Conclusions Prolonged use of benzodiazepines occurs more frequently in women, adults diagnosed several years earlier, users of selective serotonin reuptake inhibitor antidepressants and those who received a prescription from a psychiatrist. Education of clinicians, especially with regard to these populations, may decrease the overuse and misuse of benzodiazepines.
Social Internet of Things for domotics: A knowledge-based approach over LDP-CoAP

Ambient Intelligence aims at simplifying the interaction of a user with her surrounding context, minimizing the effort needed to increase comfort and assistance. Nevertheless, especially in built and structured environments, current technologies and market solutions are often far from providing the required levels of automation, coordination and adaptivity of the ambient. This paper proposes a novel semantic-based framework complying with the emerging Social Internet of Things paradigm. Infrastructured spaces can be intended as populated by device agents organized in social networks, interacting autonomously and sharing information, cooperating and orchestrating resources. A service-oriented architecture allows collaborative dissemination, discovery and composition of service/resource descriptions. The Semantic Web languages are adopted as knowledge representation layer and mobile-oriented implementations of non-monotonic inferences for semantic matchmaking are used to give decision capabilities to software agents. Finally, the Linked Data Platform (LDP) over the Constrained Application Protocol (CoAP) provides the knowledge organization and sharing infrastructure underpinning social object interactions. The framework has been implemented and tested in a home automation prototype integrating several communication protocols and off-the-shelf devices. Experiments advocate the effectiveness of the approach.
Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology which offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report the first deep learning model, named Chiron, that can directly translate the raw signal to DNA sequence without the error-prone segmentation step. We show that our model provides state-of-the-art basecalling accuracy when trained with only a small set of 4000 reads. Chiron achieves basecalling speeds of over 2000 bases per second using desktop computer graphics processing units, making it competitive with other deep-learning basecalling algorithms.
When robots are late: Configuration planning for multiple robots with dynamic goals

Unexpected contingencies in robot execution may induce a cascade of effects, especially when multiple robots are involved. In order to effectively adapt to this, robots need the ability to reason along multiple dimensions at execution time. We propose an approach to closed-loop planning capable of generating configuration plans, i.e., action plans for multirobot systems which specify the causal, temporal, resource and information dependencies between individual sensing, computation, and actuation components. The key feature which enables closed loop performance is that configuration plans are represented as constraint networks, which are shared between the planner and the executor and are continuously updated during execution. We report experiments run both in simulation and on real robots, in which a fault in one robot is compensated through different types of plan modifications at run time.
The usefulness of a bilayer artificial dermis (TERUDERMIS®) for vestibular extension in the mandible

Conventionally, autologous skin or mucous membranes from the hard palate have been used as grafts for the alveolar area. This technique, however, has the serious disadvan-tage of exposing the healthy area to secondary damage during the process of graft donation.To resolve this problem, several types of biomaterials have been developed. These are onlytemporary materials that have several disadvantages in terms of antigenicity, bio-compatibility, and handling. We used TERUDERMIS® for vestibular extension in 30 patientswho were treated with dental implants. It is composed of a collagen sponge with a siliconelayer, which is sloughed off during the healing process. This material was more effectivethan auto logous mucous membrame grafts in promoting hemostasis, relieving pain, pro-moting rapid epithelization, and inducing connective tissue. No side effects were noted. The-results suggest that the material is useful for vestibular extension of the mandible.
Map-Based Visual Exploration of Geolocated Time Series

The amount and significance of time series that are associated with specific locations, such as visitor check-ins at various places or sensor readings, have increased in many domains over the last years. Although several works exist for time series visualization and visual analytics in general, there is a lack of efficient techniques for geolocated time series in particular. In this work, we present an approach that relies on a hybrid spatial-time series index to allow for interactive map-based visual exploration and summarization of geolocated time series data. In particular, we use the BTSR-tree index, which extends the R-tree by maintaining bounds for the time series indexed at each node. We describe the structure of this index and show how it can be directly exploited to produce map-based visualizations of geolocated time series at different zoom levels efficiently. We empirically validate our approach using two real-world datasets, as well as a synthetic one that is used to test the scalability of our method.
A Survey of Fog Computing and Communication: Current Researches and Future Directions

In this survey, we discuss the evolution of distributed computing from the utility computing to the fog computing, various research challenges for the development of fog computing environments, the current status on fog computing research along with a taxonomy of various existing works in this direction. Then, we focus on the architectures of fog computing systems, technologies for enabling fog, fog computing features, security and privacy of fog, the QoS parameters, applications of fog, and give critical insights of various works done on this domain. Lastly, we briefly discuss about different fog computing associations that closely work on the development of fog based platforms and services, and give a summary of various types of overheads associated with fog computing platforms. Finally, we provide a thorough discussion on the future scopes and open research areas in fog computing as an enabler for the next generation computing paradigm.
The effect of dietary Se supplementation on the Se status and physico-chemical properties of eggs – a review

Fašiangová M. , Bořilová G. , Hulánková R. (2017): The effect of dietary Se supplementation on the Se status and physico-chemical properties of eggs – a review. Czech J. Food Sci., 35: 275–284. In the last few years, interest in the supplementation of selenium (Se) to animal feed has increased. The results of various studies have shown that eggs with defined selenium content may be used as a new potential source of this scarce element in human nutrition. The selenium content in eggs after dietary supplementation in organic form is about 22–27 μg/egg. This amount represents up to 40–50% of the Recommended Dietary Allowance, which is 55 μg of Se for the adult human. Additionally, due to its antioxidant properties, selenium contributes to the oxidation stability of fat and protein in the eggs of laying hens fed a selenium-supplemented diet. Therefore, selenium addition can affect certain properties of eggs and improve their shelf life. The results of investigations into the physico-chemical properties of different forms of Se have proven that dietary supplementation in the organic form shows higher biological availability than inorganic selenium.
Semantic Exploration of Traffic Dynamics

Given a large collection of urban datasets, how can we find their hidden correlations? For example, New York City (NYC) provides open access to taxi data from year 2012 to 2015 with about half million taxi trips generated per day. In the meantime, we have a rich set of urban data in NYC including points-of-interest (POIs), geo-tagged tweets, weather, vehicle collisions, etc. Is it possible that these ubiquitous datasets can be used to explain the city traffic? Understanding the hidden correlation between external data and traffic data would allow us to answer many important questions in urban computing such as: If we observe a high traffic volume at Madison Square Garden (MSG) in NYC, is it because of the regular peak hour or a big event being held at MSG? If a disaster weather such as a hurricane or a snow storm hits the city, how would the traffic be affected? While existing studies may utilize external datasets for prediction task, they do not explicitly seek for direct explanations from the external datasets. In this paper, we present our results in attempts to understand taxi traffic dynamics in NYC from multiple external data sources. We use four real-world ubiquitous urban datasets, including POI, weather, geo-tagged tweet, and collision records. To address the heterogeneity of ubiquitous urban data, we present carefully-designed feature representations for various datasets. Extensive experiments on real data demonstrate the explanatory power on taxi traffic by using external datasets. More specifically, our analysis suggests that POIs can well describe the regular traffic patterns. At the same time, geo-tagged tweets can explain irregular traffic caused by big events and weather can explain the abnormal traffic drop.
A Distributed Online Learning Approach for Pattern Prediction over Movement Event Streams with Apache Flink

In this paper, we present a distributed online prediction system for user-defined patterns over multiple massive streams of movement events, built using the general purpose stream processing framework Apache Flink. The proposed approach is based on combining probabilistic event pattern prediction models on multiple predictor nodes with a distributed online learning protocol in order to continuously learn the parameters of a global prediction model and share them among the predictors in a communication-efficient way. Our approach enables the collaborative learning between the predictors (i.e., "learn from each other"), thus the learning rate is accelerated with less data for each predictor. The underlying model provides online predictions about when a pattern (i.e., a regular expression over the event types) is expected to be completed within each event stream. We describe the distributed architecture of the proposed system, its implementation in Flink, and present experimental results over real-world event streams related to trajectories of moving vessels.
Characterization of mitochondrion-targeted GTPases in Plasmodium falciparum

Abstract Ribosome assembly is critical for translation and regulating the response to cellular events and requires a complex interplay of ribosomal RNA and proteins with assembly factors. We investigated putative participants in the biogenesis of the reduced organellar ribosomes of Plasmodium falciparum and identified homologues of two assembly GTPases – EngA and Obg that were found in mitochondria. Both are indispensable in bacteria and P. berghei EngA is among the ‘essential’ parasite blood stage proteins identified recently. PfEngA and PfObg1 interacted with parasite mitoribosomes in vivo. GTP stimulated PfEngA interaction with the 50S subunit of Escherichia coli surrogate ribosomes. Although PfObg1–ribosome interaction was independent of nucleotide binding, GTP hydrolysis by PfObg1 was enhanced upon ribosomal association. An additional function for PfObg1 in mitochondrial DNA transactions was suggested by its specific interaction with the parasite mitochondrial genome in vivo. Deletion analysis revealed that the positively-charged OBG (spoOB-associated GTP-binding protein) domain mediates DNA-binding. A role for PfEngA in mitochondrial genotoxic stress response was indicated by its over-expression upon methyl methanesulfonate-induced DNA damage. PfEngA had lower sensitivity to an E. coli EngA inhibitor suggesting differences with bacterial counterparts. Our results show the involvement of two important GTPases in P. falciparum mitochondrial function, with the first confirmed localization of an EngA homologue in eukaryotic mitochondria.
Congestion control in TCP/IP differentiated services network using neural network

The use of internet services for time sensitive applications like voice and video, requires the forecasting quality of service. The TCP/IP differentiated services structure is given to achieve this target. However, network congestion control is limited and comes from the high priority. Some studies are still seeking a replacement techniques such as random early detection (RED) and its modification to manage congestion. In this paper we present neural network control research results to implement RED, called NRED. We found that with neural network we can perform better for discrimination acts to cancel the packets for gathering traffic flow, and also provide better quality services to all types different traffic while ensuring high utilization.
Wolves, people, and brown bears influence the expansion of the recolonizing wolf population in Scandinavia

Interspecific competition can influence the distribution and abundance of species and the structure of ecological communities and entire ecosystems. Interactions between apex predators can have cascading effects through the entire natural community, which supports broadening the scope of conservation from single species to a much wider ecosystem perspective. However, competition between wild large carnivores can hardly be measured experimentally. In this study, we analyzed the expansion of the Scandinavian wolf (Canis lupus) population during its recovery from the early 1990s. We took into account wolf-, habitat-, human- and brown bear (Ursus arctos)-related factors, because wolf expansion occurred within an area partially sympatric with bears. Wolf pair establishment was positively related to previous wolf presence and was negatively related to road density, distance to other wolf territories, and bear density. These findings suggest that both human-related habitat modification and interspecific competition have been influential factors modulating the expansion of the wolf population. Interactions between large carnivores have the potential to affect overall biodiversity. Therefore, conservation-oriented management of such species should consider interspecific interactions, rather than focusing only on target populations of single species. Long-term monitoring data across large areas should also help quantify and predict the influence of biotic interactions on species assemblages and distributions elsewhere. This is important because interactive processes can be essential in the regulation, stability, and resilience of ecological communities.
Learning Determinantal Point Processes in Sublinear Time

We propose a new class of determinantal point processes (DPPs) which can be manipulated for inference and parameter learning in potentially sublinear time in the number of items. This class, based on a specific low-rank factorization of the marginal kernel, is particularly suited to a subclass of continuous DPPs and DPPs defined on exponentially many items. We apply this new class to modelling text documents as sampling a DPP of sentences, and propose a conditional maximum likelihood formulation to model topic proportions, which is made possible with no approximation for our class of DPPs. We present an application to document summarization with a DPP on $2^{500}$ items.
Evaluation of Torque Observer in Automotive Context

Abstract This paper describes how it is possible to estimate online the individual torque at each wheel of an automotive vehicle by using an unknown input observer together with a simple longitudinal 4 wheels model. The necessary measurements are the usual rotation speed of each wheel plus the vertical load at the center of each wheel. By doing this, this study anticipates the affordability of a new generation of wheel bearing with embedded measurements of transmitted forces. Successful simulated experimentations of the method are shown as well as its present limits.
When Do Large Buyers Pay Less? Experimental Evidence

The rise in mega-retailers has contributed to a growing literature on buyer power and large-buyer discounts. According to Rotemberg and Saloner (1986) and Snyder (1998), large buyers' ability to obtain price discounts depends on their relative (rather than absolute) size and the degree of competition between suppliers. I test experimentally comparative statics implications of this theory concerning the number of sellers and the sizes of the buyers in the market. The results track the comparative statics predictions to a surprising extent. Subtle changes in the distribution of buyer sizes or the number of suppliers can create or negate large-buyer discounts. The results highlight the previously unexplored role of the demand structure in determining buyer-size discounts. Furthermore, the experiments establish the presence of small-buyer premia, not anticipated by the theory.
Guest Editorial: Revisiting Feetham’s Criteria for Research of Families to Advance Science and Inform Policy for the Health and Well-Being of Families

Research of families focusing on health has three primary purposes: (a) to advance the knowledge of family to improve care and outcomes for families; (b) to determine sustainable interventions to improve outcomes; and (c) to inform policy to sustain change that advances health and well-being of families (Feetham, 1984; Institute of Medicine [IOM] and National Research Council [NRC], 2011). As a family nursing scholar who is a reviewer for family journals and reader of nursing, family science, and family nursing journals, I am continually struck by the number of manuscripts that do not meet these purposes. This editorial will describe how the application of the criteria for research of families that I developed can contribute to advancing the science of families and to meet these purposes of research of families (Feetham, 1984). In this guest editorial, I have deliberately referenced my earlier publications and those of other family scholars as readers may not be familiar with these landmark publications that inform the conceptual, methodological, and analytical underpinnings of research of families.