Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology-based Context-aware Recommendation Approach For Dynamic Situations Enrichment

Currently, we are living in the era of ubiquitous environments, that introduces the possibility to serve users in multiple context situations. Therefore, with this large variety of situations, diversity of preferences, and multiplicity of devices to respond to users’ needs, it becomes a necessity to have a solution that ensures the correct identification of specific situations. Otherwise, the identification process may be incomplete as it could be based beforehand on predefined situation rules, identified either by users or by the developers. Moreover, it is a cumbersome task for each user to identify his specific rules in any daily life cases. This emerges the need to recommend the right situation rule in the right context for the right person. Traditional recommender systems are mostly built to provide recommendations only based on user preferences, using a content or a collaborative approach and neglect situational context information, such as location, time and role. In this paper, we propose ontology-based dynamic context-aware recommendation system that enriches automatically profile’s situation rules in different domains (shopping, work, travel, etc.). It exploits users’ experiences by offering to the users a high level of comfort and a better-customized user experience. A new situation rules’ learning process is proposed to classify situation rules according to rule ontology before applying the recommendation process to achieve a high quality of recommendation considering three context categories (user preference, situation context, and device capability). To illustrate our approach, we have presented a case study that details how the whole process works with our recommendation system.
Effects of Dance on Pain in Patients with Fibromyalgia: A Systematic Review and Meta-Analysis

Objective The aim of this study was to perform a systematic review on the effectiveness of dance-based programs in patients with fibromyalgia, as well as calculate the overall effect size of the improvements, through a meta-analysis. Methods The Cochrane Library, Physiotherapy Evidence Database (PEDro), PubMed, TRIP, and Web of Science (WOS) were selected to identify the articles included in this systematic review and meta-analysis. A total of seven articles fulfilled all inclusion and exclusion criteria. PRISMA guidelines were followed in the data extraction process. The level of evidence was established following guidelines from the Dutch Institute for Healthcare Improvement (CBO). Results The studies were all randomized controlled trials, but not double-blind. Duration of dance programs ranged from 12 to 24 weeks. Sessions lasted between 60 and 120 minutes and were performed 1-2 times per week. The overall effect size for pain was -1.64 with a 95% CI from -2.69 to -0.59 which can be interpreted as large. In addition, significant improvements were observed in quality of life, depression, impact of the disease, anxiety, and physical function. Conclusion Dance-based intervention programs can be an effective intervention for people suffering from fibromyalgia, leading to a significant reduction of the level of pain with an effect size that can be considered as large. However, findings and conclusions from this meta-analysis must be taken with caution due to the small number of articles and the large heterogeneity.
Visual Analysis of Evolution of EEG Coherence Networks employing Temporal Multidimensional Scaling

The community structure of networks plays an important role in their analysis. It represents a high-level organization of objects within a network. However, in many application domains, the relationship between objects in a network changes over time, resulting in the change of community structure (the partition of a network), their attributes (the composition of a community and the values of relationships between communities), or both. Previous animation or timeline-based representations either visualize the change of attributes of networks or the community structure. There is no single method that can optimally show graphs that change in both structure and attributes. In this paper we propose a method for the case of dynamic EEG coherence networks to assist users in exploring the dynamic changes in both their community structure and their attributes. The method uses an initial timeline representation which was designed to provide an overview of changes in community structure. In addition, we order communities and assign colors to them based on their relationships by adapting the existing Temporal Multidimensional Scaling (TMDS) method. Users can identify evolution patterns of dynamic networks from this visualization.
FineTag: Multi-attribute Classification at Fine-grained Level in Images

In this paper, we address the extraction of the fine-grained attributes of an instance as a `multi-attribute classification' problem. To this end, we propose an end-to-end architecture by adopting the bi-linear Convolutional Neural Network with the pairwise ranking loss. This is the first time such architecture is applied for the fine-grained attributes classification problem. We compared the proposed method with a competitive deep Convolutional Neural Network baseline. Extensive experiments show that the proposed method attains/outperforms the performance of compared baseline with significantly less number of parameters ($40\times$ less). We demonstrated our approach on CUB200 birds dataset whose annotations are adapted in this work for multi-attribute classification at fine-grained level.
DNMTs inhibitor SGI-1027 induces apoptosis in Huh7 human hepatocellular carcinoma cells

SGI-1027, a novel class of relatively stable, highly lipophilic quinoline-based small-molecule inhibitors of DNA methyltransferase enzymes (DNMTs), is able to inhibit DNMTs activity, and reactivate tumor suppressor genes. However, the potential anticancer mechanisms of SGI-1027 on human hepatocellular carcinoma (HCC) cells are still not clearly understood. Thus, the objective of the present study was to clarify the inhibitory effect of SGI-1027 on the cell cycle and apoptosis of the Huh7 cell line. The results revealed that treatment with SGI-1027 resulted in a significant dose-dependent decrease in cell viability. Flow cytometric analysis identified that a 24 h treatment of SGI-1027 resulted in cell apoptosis, and typical apoptotic nucleic alterations were observed with fluorescence microscopy following terminal deoxynucleotidyl-transferase-mediated dUTP nick end labeling staining. Immunoblot analysis further demonstrated that SGI-1027 downregulated the expression of B cell lymphoma-2 and upregulated the expression of Bcl-associated X protein. However, no significant alterations of the cell cycle phases were observed. Overall, it is demonstrated that SGI-1027 causes cell apoptosis via the mitochondrial-mediated pathway, which advances current understanding of the molecular mechanisms of SGI-1027 in HCC management.
The Role of Memories in Transprecision Computing

Computing paradigms largely evolved over the last decades mainly driven by continuously increasing performance requirements, energy efficiency and power density challenges. Heterogeneous highly parallel architectures enhanced with dedicated accelerators tuned to specific applications, near-threshold computing, and recently approximate computing are examples of these new approaches. In this context the memory part was relatively untouched. However, memories play a central role in any computing system, are a major source of power consumption, and limit in many applications the overall compute performance. In this paper, we focus mainly on Dynamic Random Access Memories (DRAMs), which are today's most prominent external memories. In transprecision computing we address the DRAM memory challenge by several new approaches that are strongly related to the new techniques known on the compute side. In particular, these are the concept of approximate DRAM, advanced power-down modes and the integration of application knowledge into the memory system.
Oxidized Low-Density Lipoprotein to High-Density Lipoprotein Ratio Predicts Recurrent Stroke in Minor Stroke or Transient Ischemic Attack

Background and Purpose— Oxidized low-density lipoprotein (oxLDL) level is thought to be associated with recurrent stroke. We aimed to investigate the association between oxLDL to high-density lipoprotein (HDL) ratio and recurrent stroke in patients with minor stroke or transient ischemic attack. Methods— The study included 3019 patients with minor ischemic stroke or high-risk transient ischemic attack from the CHANCE trial (Clopidogrel in High-Risk Patients With Acute Nondisabling Cerebrovascular Events). Baseline oxLDL and HDL levels were measured. The primary outcome was any stroke within 90 days. The secondary outcomes included any stroke within 1 year and ischemic stroke and combined vascular events within 90 days and 1 year. The association between oxLDL/HDL and recurrent stroke was analyzed by using Cox proportional hazards. Results— Patients in the highest oxLDL/HDL quartile had a higher risk of recurrent stroke within 90 days (hazards ratio, 1.50; 95% CI, 1.08–2.08) compared with the lowest quartile after adjusting relevant confounding factors (P=0.02). Similar results were found for secondary outcomes (P<0.05 for all). There were no significant interaction between oxLDL/HDL and use of statins agents. Conclusions— Higher serum oxLDL/HDL level in minor stroke or transient ischemic attack was associated with increased risk of recurrent stroke in 90 days and 1 year. OxLDL/HDL may act as a powerful indicator of recurrent stroke in patients with minor stroke or transient ischemic attack. Clinical Trial Registration— URL: http://www.clinicaltrials. gov. Unique identifier: NCT00979589.
Effects of consumer use practices on nanosilver release from commercially available food contact materials

ABSTRACT Migration evaluation involving nano-enabled food contact materials (FCMs) mostly focuses on potential nanoparticle release from new unused products. This may not represent consumer use practices encountered by the FCMs in their lifecycle. In order to determine if product use impacts the release of nanoparticles or other FCM components, it is necessary to perform migration evaluations under typical consumer use scenarios. A quantitative assessment of nanoparticle release from a commercially available nanosilver-enabled cutting board was performed under five conditions intended to simulate consumer use. Knife motion, washing and scratching scenarios were simulated by linear abrasion using knife blades, scrubbing pads and tungsten carbide burr attachments, respectively. Migration was evaluated using water and 3% acetic acid as food simulants. Low concentrations of silver (Ag) were detected in water simulants, a small portion (<4 ng dm−2) in the form of silver nanoparticles (AgNPs) with particle number concentrations on the order of 106 particles dm−2. Median particle diameter was 40 nm. Nanoparticle release into water was observed under all five consumer use scenarios studied, however there was no correlation with the different levels of stress simulated.
Software Engineering Challenges of Deep Learning

Surprisingly promising results have been achieved by deep learning (DL) systems in recent years. Many of these achievements have been reached in academic settings, or by large technology companies with highly skilled research groups and advanced supporting infrastructure. For companies without large research groups or advanced infrastructure, building high-quality production-ready systems with DL components has proven challenging. There is a clear lack of well-functioning tools and best practices for building DL systems. It is the goal of this research to identify what the main challenges are, by applying an interpretive research approach in close collaboration with companies of varying size and type. A set of seven projects have been selected to describe the potential with this new technology and to identify associated main challenges. A set of 12 main challenges has been identified and categorized into the three areas of development, production, and organizational challenges. Furthermore, a mapping between the challenges and the projects is defined, together with selected motivating descriptions of how and why the challenges apply to specific projects. Compared to other areas such as software engineering or database technologies, it is clear that DL is still rather immature and in need of further work to facilitate development of high-quality systems. The challenges identified in this paper can be used to guide future research by the software engineering and DL communities. Together, we could enable a large number of companies to start taking advantage of the high potential of the DL technology.
Dilution and titration of cell-cycle regulators may control cell size in budding yeast

The size of a cell sets the scale for all biochemical processes within it, thereby affecting cellular fitness and survival. Hence, cell size needs to be kept within certain limits and relatively constant over multiple generations. However, how cells measure their size and use this information to regulate growth and division remains controversial. Here, we present two mechanistic mathematical models of the budding yeast (S. cerevisiae) cell cycle to investigate competing hypotheses on size control: inhibitor dilution and titration of nuclear sites. Our results suggest that an inhibitor-dilution mechanism, in which cell growth dilutes the transcriptional inhibitor Whi5 against the constant activator Cln3, can facilitate size homeostasis. This is achieved by utilising a positive feedback loop to establish a fixed size threshold for the START transition, which efficiently couples cell growth to cell cycle progression. Yet, we show that inhibitor dilution cannot reproduce the size of mutants that alter the cell’s overall ploidy and WHI5 gene copy number. By contrast, size control through titration of Cln3 against a constant number of genomic binding sites for the transcription factor SBF recapitulates both size homeostasis and the size of these mutant strains. Moreover, this model produces an imperfect ‘sizer’ behaviour in G1 and a ‘timer’ in S/G2/M, which combine to yield an ‘adder’ over the whole cell cycle; an observation recently made in experiments. Hence, our model connects these phenomenological data with the molecular details of the cell cycle, providing a systems-level perspective of budding yeast size control.
Effects of Selenium Supplementation on Graves' Disease: A Systematic Review and Meta-Analysis

Low selenium status is associated with increased risk of Graves' disease (GD). While several trials have discussed the efficacy of selenium supplementation for thyroid function, in GD patients, the effectiveness of selenium intake as adjuvant therapy remains unclear. In this systematic review and meta-analysis, we aimed to determine the efficacy of selenium supplementation on thyroid function in GD patients. Two reviewers searched PubMed, Web of Science, the Cochrane Central Register of Controlled Trials, and four Chinese databases for studies published up to October 31, 2017. RCTs comparing the effect of selenium supplementation on thyroid hyperfunction in GD patients on antithyroid medication to placebo were included. Serum free thyroxine (FT4), free triiodothyronine (FT3), thyrotrophic hormone receptor antibody (TRAb), and thyroid-stimulating hormone (TSH) levels were assessed. Ten trials involving 796 patients were included. Random-effects meta-analyses in weighted mean difference (WMD) were performed for 3, 6, and 9 months of supplementation and compared to placebo administration. Selenium supplementation significantly decreased FT4 (WMD=-0.86 [confidence interval (CI)-1.20 to -0.53]; p=0.756; I2=0.0%) and FT3 (WMD=-0.34 [CI-0.66 to -0.02]; p=0.719; I2=0.0%) levels at 3 months, compared to placebo administration; these findings were consistent at 6 but not 9 months. TSH levels were more elevated in the group of patients taking selenium than in the control group at 3 and 6, but not 9 months. TRAb levels decreased at 6 but not 9 months. At 6 months, patients on selenium supplementation were more likely than controls to show improved thyroid function; however, the effect disappeared at 9 months. Whether these effects correlate with clinically relevant measures remains to be demonstrated.
Matrix‐free mass spectrometry imaging of mouse brain tissue sections on silicon nanopost arrays

Mass spectrometry imaging (MSI) is capable of detection and identification of diverse classes of compounds in brain tissue sections, whereas simultaneously mapping their spatial distributions. Given the vast array of chemical components present in neurological systems, as well as the innate diversity within molecular classes, MSI platforms capable of detecting a wide array of species are useful for achieving a more comprehensive understanding of their biological roles and significance. Currently, matrix‐assisted laser desorption ionization (MALDI) is the method of choice for the molecular imaging of brain samples by mass spectrometry. However, nanostructured laser desorption ionization platforms, such as silicon nanopost arrays (NAPA), are emerging as alternative MSI techniques that can provide complementary insight into molecular distributions in the central nervous system. In this work, the molecular coverage of mouse brain lipids afforded by NAPA‐MSI is compared to that of MALDI‐MSI using two common MALDI matrices. In positive ion mode, MALDI spectra were dominated by phosphatidylcholines and phosphatidic acids. NAPA favored the ionization of phosphatidylethanolamines and glycosylated ceramides, which were poorly detected in MALDI‐MSI. In negative ion mode, MALDI favored sulfatides and free fatty acids, whereas NAPA spectra were dominated by signal from phosphatidylethanolamines. The complementarity in lipid coverages between the NAPA‐ and MALDI‐MSI platforms presents the possibility of selective lipid analysis and imaging dependent upon which platform is used. Nanofabrication of the NAPA platform offers better uniformity compared to MALDI, and the wider dynamic range offered by NAPA promises improved quantitation in imaging.
Model-Based Development with Distributed Cognition

Model Driven Engineering (MDE) allows a system to be defined using a series of models. It can refine higher level models into lower level models using model transformations, thereby automating the building of a concrete model and the software development process. This is particularly useful for Requirements Engineering since MDE can bridge the gap between early requirements models, late requirements models, and architectural models. However, requirements elicitation techniques have received little attention in terms of MDE. A major reason is the lack of a formal modelling language for some of these techniques. The definition of a metamodel is an essential step for the specification of a formal modeling language, which is a key prerequisite for Model Driven Engineering (MDE). We introduce a metamodel for Distributed Cognition, a well-known requirements elicitation technique, using the key concepts present in the framework's literature with the aim to integrate Distributed Cognition into MDE. Furthermore, we report on a preliminary case study on a software XP team to validate our metamodel.
Baicalein Enhances the Oral Bioavailability and Hepatoprotective Effects of Silybin Through the Inhibition of Efflux Transporters BCRP and MRP2

Although hepatoprotective properties of silybin are well documented, the clinical therapeutic efficacy is limited by its low bioavailability due to absorption rates, extensive phase II metabolism, and biliary excretion. As our previous study indicated that metabolic enzymes may have limited effects on the pharmacokinetic (PK) behavior of silymarin, here, we intended to increase the oral bioavailability and bio-efficacy of silybin through the inhibition of active efflux. In Caco-2 and transfected MDCKII cell models, flavone baicalein significantly inhibited the efflux of silybin as a BCRP and MRP2 inhibitor. In addition, baicalein reduced the biliary excretion index (BEI) and biliary clearance of silybin conjugates in the sandwich-cultured rat hepatocyte (SCH) model, indicating the inhibition of baicalein in biliary excretion of conjugated silybin metabolites. PK study demonstrated that baicalein significantly increased the area under the curve (AUC) and Cmax of silybin and its conjugates, suggesting enhanced absorption in vivo. Moreover, coadministration of silybin with baicalein boosted the liver protective, antioxidant, and anti-inflammatory effects of silybin in the carbon tetrachloride (CCl4)-induced liver injury model in comparison with silybin given alone. In summary, efflux transporters play a critical role in the low bioavailability of silybin, while inhibition of breast cancer resistance protein (BCRP) and multi-drug resistance protein 2 (MRP2) by baicalein can significantly increase the absorption and bio-efficacy of silybin, which provides a new combination therapeutic approach for the treatment of chronic liver diseases.
Drought effects on the stability of forest-grassland ecotones under gradual climate change

Plant communities in forest-grassland ecotones of the European Alps are already suffering from gradual climate change and will likely be exposed to more frequent and intense drought periods in the future. Yet, how gradual climate change and extreme drought will affect the stability of these plant communities is largely unknown. Here, we investigated how drought modulates the effects of gradual climate change on the long-term structural stability of these ecotone communities using a multidimensional approach. Using a spatially explicit landscape vegetation model, we simulated three drought scenarios, on top of gradual changes of climate variables, and their impacts on the dynamics of 24 plant functional groups, distinguishing between forests and grasslands, as well as different land uses. We then used n-dimensional hypervolumes to define community states under the different drought scenarios, and compared them to initial conditions to assess changes in community structural stability. In general, added drought effects did not counteract the long-term consequences of gradual climate changes, although they resulted in quantitatively different effects. Importantly, drought and climate change had non-negligible consequences for taxonomic and functional structure that differed between communities and land-use regimes. For instance, forest taxonomic structure was more overall more stable than grassland’s, despite the observed functional shifts towards more warm-adapted species compositions. Conversely, unmanaged grasslands were the least stable, suffering the loss of characteristic alpine species. Also, while frequent and severe drought regimes caused forests to become more variable in time, they had the opposite effect on grasslands. Our results agree with observations of drought- and climate-driven changes in mountain communities of the Alps, and we discuss their relevance for ecosystem management. Importantly, we demonstrate the utility of this multidimensional approach to study community stability for analysing cross-community and cross-disturbance responses to global change.