Miracle-dz/UniFAR-FTUs huggingface dataset:
Thoracic Paravertebral Block (Pvb) Analgesia

Paravertebral blockade is a safe and effective technique for intraoperative and postoperative management of acute surgical pain. The block may be performed as a single injection or a catheter may be inserted to allow for the continuous or bolus administration of a long acting local anaesthetic. This mode of analgesia is not considered to be a stand alone technique (in contrast to epidural analgesia) and most patients will require additional analgesia (usually parenteral patient controlled analgesia). Compared to epidural analgesia, the paravertebral administration of analgesia is considered to be safer in anti-coagulated patients, but the risk of local anaesthetic toxicity remains. There is little published about the care of patients receiving this form of analgesia. This article provides an overview of the technique of thoracic paravertebral analgesia, the choice of analgesics and side effects together with recommendations for patient care.
Adaptive limited feedback designs in MIMO broadcast systems with simultaneous wireless information and power transfer

This paper studies an adaptive limited feedback design in a multi-user downlink broadcast channel (BC) for simultaneous wireless information and power transfer. The objective is to maximize the sum-rate of information decoding (ID) users while maintain an minimum energy harvesting (EH) constraint for each EH user. For limited feedback environments, a total feedback bit constraint is applied at the base station and each user adopts a codebook to quantize the normalized channel state information (CSI). We first present a beamforming design for the case of one EH user and one ID user. In addition, for multiple UEs, we propose a sub-optimal beamforming design which generates a dedicated beam for each user and has low complexity. Also, we derive closed-form expressions of the achievable sum rate of the ID users and present an adaptive mode selection algorithm under different EH constraints and total number of feedback bits. Finally, simulation results verify the effectiveness of our proposed schemes.
DH-Falcon: A Language for Large-Scale Graph Processing on Distributed Heterogeneous Systems

Graph models of social information systems typically contain trillions of edges. Such big graphs cannot beprocessed on a single machine. The graph object must bepartitioned and distributed among machines and processedin parallel on a computer cluster. Programming such systemsis very challenging. In this work, we present DH-Falcon, a graph DSL (domain-specific language) which can be usedto implement parallel algorithms for large-scale graphs, tar-geting Distributed Heterogeneous (CPU and GPU) clusters. DH-Falcon compiler is built on top of the Falcon compiler, which targets single node devices with CPU and multipleGPUs. An important facility provided by DH-Falcon is that itsupports mutation of graph objects, which allows programmerto write dynamic graph algorithms. Experimental evaluationshows that DH-Falcon matches or outperforms state-of-the-art frameworks and gains a speedup of up to 13×.
Towards an Effective and Efficient Learning for Biomedical Data Classification

Nowadays a huge volume of biomedical data (images, genes, etc) are daily generated. The interpretation of such data involves a considerable expertise. The misinterpretation and/or misdetection of a suspicious clinical finding leads to increasing the negligence claims, and redundant procedures (e.g. biopsies). The analysis of biomedical data is a complex task which are performed by specialists on whose expertise degree affects the accuracy of their diagnosis. Besides, due to the huge volume of data, it is a tiresome process. To mitigate these intrinsic drawbacks Computeraided Diagnosis approaches have been proposed in the last decade, but applied without a deep analysis. It is also very common in the literature for the presentation of experimental results to rely solely on the mean of accuracy values. This procedure is not always reliable, especially for applications that require faster classifiers due to their learning-time constraints. Hence, in this paper we proposed an extensive analysis towards an effective and efficient learning for biomedical data classification. To do so, several public biomedical datasets were used against different supervised classifiers, taking into account accuracies and computational times obtained throughout the learning process.
An extraction and unification methodology for social networks data: an application to public security

Social media are invaded our daily life where millions of users are subscribed. Those online sites provide great tool for users to communicate with others from all over the world, share information, and express their opinion. Unfortunately, this ease of access and availability of information is exploited by malicious users to spread their radical ideas and prepare for terrorist attacks. In this paper, we have proposed a new methodology for data extraction, annotation, and unification in order to identify suspicious content from online social media threatening public security. To the best of our knowledge, there is no specific data collected method from social media devoted to suspicious user profile extraction. Also, implying an expert for data annotation for a security purposes remain a new research task that necessitate expertise and knowledge. In this paper, we tackle this research area by collecting suspicious content from different social media.
Balanced Cluster Lifetime Prolonging Protocol for Wireless Sensor Network

sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in diverse conditions. One of the crucial matter in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a balanced cluster routing protocol called balance cluster lifetime prolonging protocol (BCLPP). In BCLPP, we divide the network into four static quadrants which have almost equal number of nodes, which reduce the overhead of dynamic clustering thus increase the lifetime of our network. Simulation-based evaluations are performed to compare the performance of BCLPP against Energy-Efficient Protocol with Static Clustering for Wireless Sensor Networks (EEPSC) Our experiment results show that BCLPP outperforms EEPSC in terms of network lifetime and power consumption minimization. Keywordstechnique, Energy efficiency, network lifetime and wireless sensor network.
Range shifting species reduce phylogenetic diversity in high latitude communities via competition

Under anthropogenic climate change, many species are expanding their ranges to higher latitudes and altitudes, resulting in novel species interactions. The consequences of these range shifts for native species, patterns of local biodiversity and community structure in high latitude ecosystems are largely unknown but critical to understand in light of widespread poleward expansions by many warm-adapted generalists. Using niche modelling, phylogenetic methods, and field and laboratory studies, we investigated how colonization of Scotland by a range expanding damselfly, Ischnura elegans, influences patterns of competition and niche shifts in native damselfly species, and changes in phylogenetic community structure. Colonization by I. elegans was associated with reduced population density and niche shifts in the resident species least related to I. elegans (Lestes sponsa), reflecting enhanced competition. Furthermore, communities colonized by I. elegans exhibited phylogenetic underdispersion, reflecting patterns of relatedness and competition. Our results provide a novel example of a potentially general mechanism whereby climate change-mediated range shifts can reduce phylogenetic diversity within high latitude communities, if colonizing species are typically competitively superior to members of native communities that are least-closely related to the colonizer.
Social Commerce Hybrid Product Recommender

vision for Web 3.0 (also known as Semantic Web) is the ability to create meaning out of huge quantity of qualitative data. Existing data can be interconnected for further uses. Web 2.0 focused on the users interaction with others whereas Web 3.0 focus more on the users themselves. The advantages of Semantic Web and E-commerce give rise to social commerce (also referred as f-commerce). The future of business lies on the "social" factor and it is this factor which gives rise to a new kind of connected consumers who are becoming influential in their own right. This paper explores a very specific instance of Semantic Web - Social Recommender System. This paper discusses the likelihood of converting social data into quantitative information and using this information to power social recommendations. This paper first outlines the benefits of social commerce over e- commerce platform. Then the related literature work regarding hybrid recommenders is discussed. Next it is discussed how to predict ratings from a user-item rating network and friend's network and then how to unify similarity matrices obtained from different networks. And lastly this paper covers the social hybrid product recommender algorithm and its experimental evaluations to predict its efficiency. Keywordscommerce, hybrid product recommender, f-commerce, homophily, Cosine similarity, Smith Waterman string similarity measure, unipartite graph, bipartite graph
An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting

Abstract Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.
Selection from heaps, row-sorted matrices and X+Y using soft heaps

We use soft heaps to obtain simpler optimal algorithms for selecting the $k$-th smallest item, and the set of~$k$ smallest items, from a heap-ordered tree, from a collection of sorted lists, and from $X+Y$, where $X$ and $Y$ are two unsorted sets. Our results match, and in some ways extend and improve, classical results of Frederickson (1993) and Frederickson and Johnson (1982). In particular, for selecting the $k$-th smallest item, or the set of~$k$ smallest items, from a collection of~$m$ sorted lists we obtain a new optimal "output-sensitive" algorithm that performs only $O(m+\sum_{i=1}^m \log(k_i+1))$ comparisons, where $k_i$ is the number of items of the $i$-th list that belong to the overall set of~$k$ smallest items.
Defective Repair of Uracil Causes Telomere Defects in Mouse Hematopoietic Cells*

Background: Telomeres may be susceptible to uracil misincorporation, which is removed by uracil DNA glycosylase (UNG). Results: UNG deficiency increases uracil in telomeres and causes alterations in multiple aspects of telomere maintenance in proliferating mouse hematopoietic cells. Conclusion: Accumulation of uracil interferes with telomere maintenance. Significance: UNG-initiated base excision repair is necessary for the preservation of telomere integrity. Uracil in the genome can result from misincorporation of dUTP instead of dTTP during DNA synthesis, and is primarily removed by uracil DNA glycosylase (UNG) during base excision repair. Telomeres contain long arrays of TTAGGG repeats and may be susceptible to uracil misincorporation. Using model telomeric DNA substrates, we showed that the position and number of uracil substitutions of thymine in telomeric DNA decreased recognition by the telomere single-strand binding protein, POT1. In primary mouse hematopoietic cells, uracil was detectable at telomeres, and UNG deficiency further increased uracil loads and led to abnormal telomere lengthening. In UNG-deficient cells, the frequencies of sister chromatid exchange and fragility in telomeres also significantly increased in the absence of telomerase. Thus, accumulation of uracil and/or UNG deficiency interferes with telomere maintenance, thereby underscoring the necessity of UNG-initiated base excision repair for the preservation of telomere integrity.
Serum amyloid A3 is a high density lipoprotein-associated acute-phase protein

Serum amyloid A (SAA) is a family of acute-phase reactants. Plasma levels of human SAA1/SAA2 (mouse SAA1.1/2.1) can increase ≥1,000-fold during an acute-phase response. Mice, but not humans, express a third relatively understudied SAA isoform, SAA3. We investigated whether mouse SAA3 is an HDL-associated acute-phase SAA. Quantitative RT-PCR with isoform-specific primers indicated that SAA3 and SAA1.1/2.1 are induced similarly in livers (∼2,500-fold vs. ∼6,000-fold, respectively) and fat (∼400-fold vs. ∼100-fold, respectively) of lipopolysaccharide (LPS)-injected mice. In situ hybridization demonstrated that all three SAAs are produced by hepatocytes. All three SAA isoforms were detected in plasma of LPS-injected mice, although SAA3 levels were ∼20% of SAA1.1/2.1 levels. Fast protein LC analyses indicated that virtually all of SAA1.1/2.1 eluted with HDL, whereas ∼15% of SAA3 was lipid poor/free. After density gradient ultracentrifugation, isoelectric focusing demonstrated that ∼100% of plasma SAA1.1 was recovered in HDL compared with only ∼50% of SAA2.1 and ∼10% of SAA3. Thus, SAA3 appears to be more loosely associated with HDL, resulting in lipid-poor/free SAA3. We conclude that SAA3 is a major hepatic acute-phase SAA in mice that may produce systemic effects during inflammation.
Model checking rational agents

Agent-oriented programming techniques seem appropriate for developing systems that operate in complex, dynamic, and unpredictable environments. We aim to address this requirement by developing model-checking techniques for the (automatic or semiautomatic) verification of rational-agent systems written in a logic-based agent-oriented programming language. Typically, developers apply model-checking techniques to abstract models of a system rather than the system implementation. Although this is important for detecting design errors at an early stage, developers might still introduce errors during coding. In contrast, developers can directly apply our model-checking techniques to systems implemented in an agent-oriented programming language, automatically verifying agent systems without the usual gap between design and implementation. We developed our techniques for AgentSpeak, a rational-agent programming language based on the AgentSpeak (L) abstract agent-oriented programming language. AgentSpeak shares many features of the agent-oriented programming paradigm. Similarly, we've developed techniques for automatically translating AgentSpeak programs into the model specification language of existing model-checking systems. In this way, we reduce the problem of verifying that an AgentSpeak system has certain BDI logic properties to a conventional LTL model-checking problem.
Relation between cardiovascular disease risk factors and epicardial adipose tissue density on cardiac computed tomography in patients at high risk of cardiovascular events

Background The radiodensity of epicardial adipose tissue may provide information on cardiovascular risk in addition to epicardial adipose tissue volume. The aim of this study was to quantify the relation between cardiovascular risk factors and the radiodensity of epicardial adipose tissue in patients at high risk of cardiovascular disease. Design This was a cross-sectional study in 140 patients at high risk of cardiovascular disease. Methods Patients from the Secondary Manifestations of ARTerial disease (SMART) cohort study were invited to undergo cardiac computed tomography angiography. The radiodensity (in Hounsfield units; HU) and volume (in cm3) of epicardial adipose tissue were quantified semi-automatically. Multivariable linear regression was used to quantify the relation between cardiovascular risk factors and the radiodensity of epicardial adipose tissue. Results The cardiovascular risk factors most strongly associated with epicardial adipose tissue density were sex, body mass index and visceral fat, with a lower adipose tissue attenuation of 3.5 HU (95% confidence interval (CI) 2.0–5.0 HU) for female sex, 1.6 HU (95%CI 0.2–2.9 HU) for body mass index >25 kg/m2 and 1.3 HU (95% CI 0.6–2.0 HU) for a one standard deviation higher quantity of visceral fat, adjusted for age, sex, coronary artery bypass graft history and epicardial adipose tissue volume. Conclusion Low epicardial adipose tissue computed tomography attenuation is associated with an adverse cardiovascular risk factor profile in patients at high risk of cardiovascular disease, independent of the volume of epicardial adipose tissue and waist circumference. These findings support the potential role for epicardial adipose tissue radiodensity as a valid biomarker of cardiovascular risk. Adipose tissue radiodensity may be a more sensitive marker than epicardial adipose tissue volume with which to study the contribution of epicardial adipose tissue to the coronary atheromatous disease process.
Mesoporous Silica and Organosilica Nanoparticles: Physical Chemistry, Biosafety, Delivery Strategies, and Biomedical Applications

Predetermining the physico‐chemical properties, biosafety, and stimuli‐responsiveness of nanomaterials in biological environments is essential for safe and effective biomedical applications. At the forefront of biomedical research, mesoporous silica nanoparticles and mesoporous organosilica nanoparticles are increasingly investigated to predict their biological outcome by materials design. In this review, it is first chronicled that how the nanomaterial design of pure silica, partially hybridized organosilica, and fully hybridized organosilica (periodic mesoporous organosilicas) governs not only the physico‐chemical properties but also the biosafety of the nanoparticles. The impact of the hybridization on the biocompatibility, protein corona, biodistribution, biodegradability, and clearance of the silica‐based particles is described. Then, the influence of the surface engineering, the framework hybridization, as well as the morphology of the particles, on the ability to load and controllably deliver drugs under internal biological stimuli (e.g., pH, redox, enzymes) and external noninvasive stimuli (e.g., light, magnetic, ultrasound) are presented. To conclude, trends in the biomedical applications of silica and organosilica nanovectors are delineated, such as unconventional bioimaging techniques, large cargo delivery, combination therapy, gaseous molecule delivery, antimicrobial protection, and Alzheimer's disease therapy.
Using SPM 12’s Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users

Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely P < 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and statisticians have considered Bayesian inference as a candidate methodology. However, few previous studies have attempted to provide end users of fMRI analysis tools, such as SPM 12, with practical guidelines about how to conduct Bayesian inference. In the present study, we aim to demonstrate how to utilize Bayesian inference, Bayesian second-level inference in particular, implemented in SPM 12 by analyzing fMRI data available to public via NeuroVault. In addition, to help end users understand how Bayesian inference actually works in SPM 12, we examine outcomes from Bayesian second-level inference implemented in SPM 12 by comparing them with those from classical second-level inference. Finally, we provide practical guidelines about how to set the parameters for Bayesian inference and how to interpret the results, such as Bayes factors, from the inference. We also discuss the practical and philosophical benefits of Bayesian inference and directions for future research.
GPU-based trimming and tessellation of NURBS and T-Spline surfaces

As there is no hardware support neither for rendering trimmed NURBS -- the standard surface representation in CAD -- nor for T-Spline surfaces the usability of existing rendering APIs like OpenGL, where a run-time tessellation is performed on the CPU, is limited to simple scenes. Due to the irregular mesh data structures required for trimming no algorithms exists that exploit the GPU for tessellation. Therefore, recent approaches perform a pretessellation and use level-of-detail techniques. In contrast to a simple API these methods require tedious preparation of the models before rendering and hinder interactive editing. Furthermore, due to the tremendous amount of triangle data smooth zoom-ins from long shot to close-up are not possible, In this paper we show how the trimming region can be defined by a trim-texture that is dynamically adapted to the required resolution and allows for an efficient trimming of surfaces on the GPU. Combining this new method with GPU-based tessellation of cubic rational surfaces allows a new rendering algorithm for arbitrary trimmed NURBS and T-Spline surfaces with prescribed error in screen space on the GPU. The performance exceeds current CPU-based techniques by a factor of up to 1000 and makes real-time visualization of real-world trimmed NURBS and T-Spline models possible on consumer-level graphics cards.
Customizable Parallel Execution of Scientific Stream Queries

Scientific applications require processing high-volume on-line streams of numerical data from instruments and simulations. We present an extensible stream database system that allows scalable and flexible continuous queries on such streams. Application dependent streams and query functions are defined through an object-relational model. Distributed execution plans for continuous queries are described as high-level data flow distribution templates. Using a generic template we define two partitioning strategies for scalable parallel execution of expensive stream queries: window split and window distribute. Window split provides operators for parallel execution of query functions by reducing the size of stream data units using application dependent functions as parameters. By contrast, window distribute provides operators for customized distribution of entire data units without reducing their size. We evaluate these strategies for a typical high volume scientific stream application and show that window split is favorable when expensive queries are executed on limited resources, while window distribution is better otherwise.