Miracle-dz/UniFAR-FTUs huggingface dataset:
Powering RSS Aggregators with Ontologies - A Case for the RSSOWL Aggregator

Content syndication through RSS is gaining wide acceptance, and it is envisaged that feed aggregators will be provided as a commodity in future browsers. As we consume more of our information by way of RSS feeds, search mechanisms other than simple keyword search will be required. To this end, advances in semantic tooling can effectively improve the current state of the art in feed aggregators. This work reports on the benefits of making a popular RSS aggregator, RSSOwl, ontology-aware. The paper uses three common functions, namely, semantic view, semantic navigation and semantic query, to illustrate how RSS aggregators can be “ontology powered”. The outcome is that location, browsing and rendering of RSS feeds are customised to the conceptual model of the reader, making RSS aggregators a powerful companion to face the “RSSosphere”. The system has been fully implemented, and successfully tested by distinct users.
Interactive Effects Between Exercise and Serotonergic Pharmacotherapy on Cortical Reorganization After Spinal Cord Injury

Background. In rat models of spinal cord injury, at least 3 different strategies can be used to promote long-term cortical reorganization: (1) active exercise above the level of the lesion; (2) passive exercise below the level of the lesion; and (3) serotonergic pharmacotherapy. Whether and how these potential therapeutic strategies—and their underlying mechanisms of action—interact remains unknown. Methods. In spinally transected adult rats, we compared the effects of active exercise above the level of the lesion (treadmill), passive exercise below the level of the lesion (bike), serotonergic pharmacotherapy (quipazine), and combinations of the above therapies (bike+quipazine, treadmill+quipazine, bike+treadmill+quipazine) on long-term cortical reorganization (9 weeks after the spinal transection). Cortical reorganization was measured as the percentage of cells recorded in the deafferented hindlimb cortex that responded to tactile stimulation of the contralateral forelimb. Results. Bike and quipazine are “competing” therapies for cortical reorganization, in the sense that quipazine limits the cortical reorganization induced by bike, whereas treadmill and quipazine are “collaborative” therapies, in the sense that the reorganization induced by quipazine combined with treadmill is greater than the reorganization induced by either quipazine or treadmill. Conclusions. These results uncover the interactive effects between active/passive exercise and serotonergic pharmacotherapy on cortical reorganization after spinal cord injury, emphasizing the importance of understanding the effects of therapeutic strategies in spinal cord injury (and in other forms of deafferentation) from an integrated system-level approach.
The Association Between the Dosage of SGLT2 Inhibitor and Weight Reduction in Type 2 Diabetes Patients: A Meta‐Analysis

Sodium glucose cotransporter 2 (SGLT2) inhibitors may induce urinary glucose excretion via the inhibition of renal glucose reabsorption, improve glycemic control, and lower body weight. The aim of this meta‐analysis was to evaluate weight changes in patients who received different dosages of SGLT2 inhibitors.
Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments

Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. To cope with this limitation, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops an online multi-kernel learning scheme to infer the intended nonlinear function `on the fly. ' To further boost performance in non-stationary environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is developed with affordable computation and memory complexity. Performance is analyzed in terms of both static and dynamic regret. To our best knowledge, AdaRaker is the first algorithm that can optimally track nonlinear functions in non-stationary settings with theoretical guarantees. Numerical tests on real datasets are carried out to showcase the effectiveness of the proposed algorithms.
Deep Motion Boundary Detection

Motion boundary detection is a crucial yet challenging problem. Prior methods focus on analyzing the gradients and distributions of optical flow fields, or use hand-crafted features for motion boundary learning. In this paper, we propose the first dedicated end-to-end deep learning approach for motion boundary detection, which we term as MoBoNet. We introduce a refinement network structure which takes source input images, initial forward and backward optical flows as well as corresponding warping errors as inputs and produces high-resolution motion boundaries. Furthermore, we show that the obtained motion boundaries, through a fusion sub-network we design, can in turn guide the optical flows for removing the artifacts. The proposed MoBoNet is generic and works with any optical flows. Our motion boundary detection and the refined optical flow estimation achieve results superior to the state of the art.
Identifying the transition between single and multiple mating of queens in fungus-growing ants

Obligate mating of females (queens) with multiple males has evolved only rarely in social Hymenoptera (ants, social bees, social wasps) and for reasons that are fundamentally different from those underlying multiple mating in other animals. The monophyletic tribe of ‘attin’) fungus–growing ants is known to include evolutionarily derived genera with obligate multiple mating (the Acromyrmex and Atta leafcutter ants) as well as phylogenetically basal genera with exclusively single mating (e.g. Apterostigma, Cyphomyrmex, Myrmicocrypta). All attine genera share the unique characteristic of obligate dependence on symbiotic fungus gardens for food, but the sophistication of this symbiosis differs considerably across genera. The lower attine genera generally have small, short–lived colonies and relatively non–specialized fungal symbionts (capable of living independently of their ant hosts), whereas the four evolutionarily derived higher attine genera have highly specialized, long–term clonal symbionts. In this paper, we investigate whether the transition from single to multiple mating occurred relatively recently in the evolution of the attine ants, in conjunction with the novel herbivorous ‘leafcutte’ niche acquired by the common ancestor of Acromyrmex and Atta, or earlier, at the transition to rearing specialized long–term clonal fungi in the common ancestor of the larger group of higher attines that also includes the genera Trachymyrmex and Sericomyrmex. We use DNA microsatellite analysis to provide unambiguous evidence for a single, late and abrupt evolutionary transition from exclusively single to obligatory multiple mating. This transition is historically correlated with other evolutionary innovations, including the extensive use of fresh vegetation as substrate for the fungus garden, a massive increase in mature colony size and morphological differentiation of the worker caste.
Lysibodies are IgG Fc fusions with lysin binding domains targeting Staphylococcus aureus wall carbohydrates for effective phagocytosis

Significance Antibiotic resistance is an ever-increasing problem; for certain pathogens, few treatment options remain. Vaccines and therapeutic antibodies represent important alternatives to antibiotics, yet despite extensive effort no approved vaccine is available for Staphylococcus aureus. Although wall carbohydrates are ideal immunotherapeutic targets due to their abundance and high level of conservation, their poor immunogenicity compared with conventional protein targets complicates the production of effective antibodies. The approach presented here fuses the high-affinity binding domains from bacteriophage lysins and autolysins that recognize specific cell wall carbohydrate epitopes to IgG Fc, creating effective therapeutic antibodies, or lysibodies. This approach is generalizable, allowing production of antibodies to poorly immunogenic carbohydrate epitopes of many Gram-positive pathogens. Lysibodies thus represent a broad class of anti-infectives. The cell wall of Gram-positive bacteria contains abundant surface-exposed carbohydrate molecules that are highly conserved within and often across species. The potential therapeutic usefulness of high-affinity antibodies to cell wall carbohydrates is unquestioned, however obtaining such antibodies is challenging due to the poor overall immunogenicity of these bacterial targets. Autolysins and phage lysins are peptidoglycan hydrolases, enzymes that have evolved over a billion years to degrade bacterial cell wall. Such wall hydrolases are modular enzymes, composed of discrete domains for high-affinity binding to cell wall carbohydrates and cleavage activity. In this study, we demonstrate that binding domains from autolysins and lysins can be fused to the Fc region of human IgG, creating a fully functional homodimer (or “lysibody”) with high-affinity binding and specificity for carbohydrate determinants on the bacterial surface. Furthermore, we demonstrate that this process is reproducible with three different binding domains specific to methicillin-resistant Staphylococcus aureus (MRSA). Cell-bound lysibodies induced the fixation of complement on the bacterial surface, promoted phagocytosis by macrophages and neutrophils, and protected mice from MRSA infection in two model systems. The lysibody approach could be used to target a range of difficult-to-treat pathogenic bacteria, given that cell wall hydrolases are ubiquitous in nature.
Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach

We consider the problem of minimizing a convex function over the intersection of finitely many simple sets which are easy to project onto. This is an important problem arising in various domains such as machine learning. The main difficulty lies in finding the projection of a point in the intersection of many sets. Existing approaches yield an infeasible point with an iteration-complexity of $O(1/\varepsilon^2)$ for nonsmooth problems with no guarantees on the in-feasibility. By reformulating the problem through exact penalty functions, we derive first-order algorithms which not only guarantees that the distance to the intersection is small but also improve the complexity to $O(1/\varepsilon)$ and $O(1/\sqrt{\varepsilon})$ for smooth functions. For composite and smooth problems, this is achieved through a saddle-point reformulation where the proximal operators required by the primal-dual algorithms can be computed in closed form. We illustrate the benefits of our approach on a graph transduction problem and on graph matching.
The HIV-1 Tat Protein Is Monomethylated at Lysine 71 by the Lysine Methyltransferase KMT7*

The HIV-1 transactivator protein Tat is a critical regulator of HIV transcription primarily enabling efficient elongation of viral transcripts. Its interactions with RNA and various host factors are regulated by ordered, transient post-translational modifications. Here, we report a novel Tat modification, monomethylation at lysine 71 (K71). We found that Lys-71 monomethylation (K71me) is catalyzed by KMT7, a methyltransferase that also targets lysine 51 (K51) in Tat. Using mass spectrometry, in vitro enzymology, and modification-specific antibodies, we found that KMT7 monomethylates both Lys-71 and Lys-51 in Tat. K71me is important for full Tat transactivation, as KMT7 knockdown impaired the transcriptional activity of wild type (WT) Tat but not a Tat K71R mutant. These findings underscore the role of KMT7 as an important monomethyltransferase regulating HIV transcription through Tat.
Combination antiplatelet treatment in coronary artery disease patients: A necessary evil or an overzealous practice?

Abstract In seeking to improve care in coronary artery disease patients, further platelet inhibition has been occasionally applied beyond that provided by aspirin and a P2Y12 receptor antagonist. This review aims to offer insights about the rationale, the efficacy and safety of combination antiplatelet therapy, involving three or more agents. Overall, the use of glycoprotein (GP) IIb/IIIa inhibitors did not significantly modify the treatment effect of different antiplatelet strategies, including double vs standard clopidogrel, prasugrel vs clopidogrel, ticagrelor vs clopidogrel, cangrelor vs clopidogrel, and vorapaxar vs placebo. With the caveat that the use of GP IIb/IIIa inhibitor was not randomized, adding such an agent to aspirin and a P2Y12 receptor antagonist appears to carry a significantly increased bleeding potential. Moreover, adding vorapaxar to aspirin- and clopidogrel-treated patients is associated with more bleeding events, while the bleeding potential is further exacerbated in cases of quadruplicate antiplatelet treatment including aspirin, clopidogrel, vorapaxar, and a GP IIb/IIIa inhibitor. In ST-segment elevation, myocardial infarction patients’ administration of an intravenous antiplatelet agent (GP IIb/IIIa inhibitor or cangrelor), in addition to aspirin and a P2Y12 receptor antagonist, efficiently bridges the pharmacodynamic gap of oral agents. Cilostazol on top of aspirin and clopidogrel appears to be safe, although of questionable clinical benefit. In conclusion, combination antiplatelet therapy should be reserved only for selected cases and following thoughtful consideration of the associated risk/benefit ratio.
Thermal-Dynamic Stability Analysis for the Enclosed Isolated-Phase Bus Bar Based on the Subsegment Calculation Model

As a part of the power system, the stability of the enclosed isolated-phase bus bar (EIPB) plays an important role in the working safety of the power system. Usually, the dynamic-thermal stability of EIPB is analyzed by the theoretical analysis or the finite-element method (FEM). The dynamic stability can be calculated by 2-D model. For the complex structure and huge size of EIPB, large synchronous generator-side EIPB in power system is rarely analyzed. In this paper, a new co-simulation method of EIPB is studied. The subsegments EIPB model based on FEM is established, and the temperature distribution in joint surfaces is transmitted. The method is applied to a certain type of EIPB in power system. The FEM model is simulated. The temperature distribution of EIPB under certain cooling condition is also calculated. Based on the thermal results, the dynamic stability of EIPB is calculated to analyze the electrodynamic force under short-circuit condition. In addition, the feasibility and the validity of the method are proved by the test EIPB.
Data fusion for 3D thermal imaging using depth and stereo camera for robust self-localization

Infrared thermography is a common method of measuring surface temperature distributions. New approaches to the three-dimensional (3D) recording of these measurements have been developed in the last decade. A given 3D real-time imaging system [1] consisting of an infrared (IR) camera and a near-infrared (NIR) depth sensor is the object of investigation. The system uses an improved KinectFusion processing algorithm and its Iterative Closest Point (ICP) method for self-localization. The aim of this work is to reduce the impact of correspondence problems when scanning objects with few geometric features on 3D modeling. In order to achieve robust self-localization, a stereo camera is integrated into the system and its localization data is fused with the existing system data. By means of indoor and outdoor test scenarios the system shows its ability to bridge sections of ICP localization problems.
ILLIA: Enabling  $k$ -Anonymity-Based Privacy Preserving Against Location Injection Attacks in Continuous LBS Queries

With the increasing popularity of location-based services (LBSs), it is of paramount importance to preserve one’s location privacy. The commonly used location privacy preserving approach, location <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-anonymity, strives to aggregate the queries of <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> nearby users within a so-called cloaked region via a trusted third-party anonymizer. As such, the probability to identify the location of every user involved is no more than <inline-formula> <tex-math notation="LaTeX">${1/k}$ </tex-math></inline-formula>, thus offering privacy preservation for users. One inherent limitation of <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-anonymity, however, is that all users involved are assumed to be trusted and report their real locations. When location injection attacks (LIAs) are conducted, where the untrusted users inject fake locations (along with fake queries) to the anonymizer, the probability of disclosing one’s location privacy could be greatly more than <inline-formula> <tex-math notation="LaTeX">${1/k}$ </tex-math></inline-formula>, yielding a much higher risk of privacy leakage. To tackle this problem, in this paper we present ILLIA, the first work that enables <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-anonymity-based privacy preservation against LIA in continuous LBS queries. Central to the ILLIA idea is to explore the pattern of the users’ mobility in continuous LBS queries. With a thorough understanding of the users’ mobility similarity, a credibility-based <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-anonymity scheme is developed, such that ILLIA is able to defense against LIA without requiring in advance knowledge of how fake locations are manipulated while still maintaining high quality of services. Both the effectiveness and the efficiency of ILLIA are validated by extensive simulations on real world dataset loc-Gowalla.
Network-Word Embedding for Dynamic Text Attributed Networks

Network embedding enables to apply off-the-shelf machine learning methods to the nodes on the network. Leveraging the textual information associated with nodes into network embedding methods is advantageous. However, only a few works try to leverage textual information to network embeddings. Moreover, the structure of networks and associated texts could be dynamically changed over time, this property leads shifts of the vector representation of nodes and words. However, to the best of our knowledge, none of previous network embedding methods considers chronological changes of vector representations of nodes and words. In this paper, we propose a dynamic text attribute network embedding method, which embeds the nodes and the words in a cooperative manner and takes chronological changes of vector representations of nodes and words into account. Experimental results show that (1) vector representations of nodes of our method achieve higher accuracy than baseline methods in classification and clustering tasks; (2) The vector representations of nodes and words successfully capture semantic similarity; And (3) our method successfully capture the chronological change of the vector representations over time.
A finely machined toothed silver electrode surface for improved acquisition of EMG signals

With the growing interest in field of bio signal applications, it has become increasingly important to focus on improved signal acquisition mechanisms. The increase of research in EMG measurement techniques puts emphasis on bio signal detection materials and surface designs that come in contact with the skin. The work presented in this paper introduces an EMG detection surface developed by fine machining of solid silver to produce a millimeter scale toothed electrode. The toothed electrode surface consists of a higher surface area as compared to its flatter counterpart. The surface was built to not only improve the contact with skin, but also to improve signal quality. The paper provides a comparison of the electrode with gelled type EMG electrodes and a silver surface of same dimensions but with a flatter surface. Another important factor in EMG effectiveness depends upon the electrode skin impedance. Hence, a comparison of the introduced EMG electrode surface is provided in terms of electrode skin impedance using a flat silver surface of same dimensions and gelled EMG electrodes. A detailed account of the comparisons suggest that the results from the proposed electrode surface are close to gelled EMG electrodes and are better than flat silver surface.
Methods and algorithms of segmentation and deconvolution for quantitative analysis of Tissue Microarray images. (Méthodes et algorithmes de segmentation et de déconvolution d'images pour l'analyse quantitative de Tissue Microarrays)

In this chapter, we propose an Automatic, fast and robust TMA De-arraying (ATMAD) approach dedicated to images acquired with bright field and fluorescence microscopes (or scanners). First, tissue samples are localized in the large image by applying a locally adaptive thresholding on the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametric shape model is considered for segmenting ellipse-shaped objects at each detected position. Segmented objects that do not meet the size and the roundness criteria are discarded from the list of tissue samples before being matched with the design grid. Sample matching is performed by estimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimated deformation, the true tissue samples that were preliminary rejected in the early image processing step are recognized by running a second segmentation step. By combining wavelet-based detection, active contour segmentation, and thin-plate spline interpolation, our approach is able to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background and non-linear deformation of TMA grid. In addition, the deformation estimation produces quantitative information to asset the manufacturing quality of TMAs.
Identifying priority conservation areas for the American burying beetle, Nicrophorus americanus (Coleoptera: Silphidae), a habitat generalist

Conservation efforts leading to the recovery of the federally endangered American burying beetle (ABB), Nicrophorus americanus Olivier, have been challenging because of the unknown causes of its decline, difficulty in establishing habitat requirements, and unclear population distribution across the species’ range. Extant populations of this widespread generalist species occur in broadly separated regions of North America with varying habitat characteristics. A habitat suitability model for ABB in the Nebraska Sandhills was developed over the course of 3 years resulting in a final cross-validated spatial model. The succession of models from 2009 to 2011 indicated that most of the predictive variables stayed constant, but biased sampling and extrapolation areas affected classifier values differently. Variables associated with ABB occurrence were loamy sand, wetland and precipitation. Five variables, loam soil, agriculture, woodland, the average maximum temperature, and urban development, were associated with ABB absence. The 2011 cross-validated model produced an AUC value of 0.82 and provided areas designated as highly likely to support ABBs. By limiting the model extent to the Sandhills ecoregion and using threshold-dependent classifiers, the final habitat suitability model could be an important resource for wildlife managers engaged in the recovery of this habitat generalist.