Miracle-dz/UniFAR-FTUs huggingface dataset:
Cytokine and microRNA levels during different periods of paradoxical sleep deprivation and sleep recovery in rats

Background Sleep has a fundamental role in the regulation of homeostasis. The aim of this study was to assess the effect of different periods of paradoxical sleep deprivation (PSD) and recovery on serum levels of cytokines and miRNAs related to inflammatory responses. Methods Male Wistar rats were submitted to a PSD of 24, 96, or 192 h, or of 192 h followed by 20 days of recovery (192 h PSD+R). The concentrations of corticosterone, cytokines (IL-6, TNF, IL-10, Adiponectin) and miRNAs (miR-146a, miR-155, miR-223, miR-16, miR-126, miR-21) in serum were evaluated. Results At PSD 24 h a significant increase of IL-6 and decrease of IL-10 were observed. At PSD 96h adiponectin increased. At 192 h of PSD IL-6 increased significantly again, accompanied by a threefold increase of IL-10 and an increase of serum corticosterone. After 20 days of recovery (192 h PSD+R) corticosterone, IL-6 and TNF levels increased significantly, while IL-10 decreased also significantly. Regarding the miRNAs at 24 h of PSD serum miR-146a, miR-155, miR-223, and miR-16 levels all increased. At 96 h of PSD miR-223 decreased. At 192 h of PSD decreases in miR-16 and miR-126 were observed. After recovery serum miR-21 increased and miR-16 decreased. Conclusion PSD induces a dynamic response likely reflecting the induced cellular stress and manifested as variating hormonal and inflammatory responses. Sleep deprivation disturbed corticosterone, cytokine and miRNA levels in serum related to the duration of sleep deprivation, as short-term PSD produced effects similar to those of an acute inflammatory response and long-term PSD induced long-lasting disturbances of biological mediators.
Personal factors associated with the attitudes of nurses towards patients with obesity: a literature review

This literature review explores the association between body mass index and self‐esteem in nurses, alongside their attitudes towards patients who have obesity. These variables have either previously been linked to weight bias, or with prejudice more generally, but findings in this context are inconclusive. Searches were conducted through Psychology Cross Search, PubMed, Web of Science, Proquest Allied Health Professionals and Google Scholar. Ten studies met the inclusion criteria and were tabulated and critiqued using appropriate appraisal tools. The literature covered a time span of over 30 years with the type and quality of study methodologies varying. Research was rarely underpinned by theory. As such, no consensus was reached in drawing together the findings but the direction of future research needed to address these issues is discussed. Further research should focus on developing the literature using an appropriate theoretical underpinning in order to better understand the research that has begun further afield.
Multi-Priority Cartesian Impedance Control Based on Quadratic Programming Optimization

In this work we introduced a prioritized Cartesian impedance control under the framework of the Quadratic Programming (QP) optimization. In particular, we present a formulation which is simpler than full inverse dynamics, avoids any matrix pseudo-inversion, inverse kinematics computation and considers strict priorities among tasks. Our formulation is based on QP optimization permitting to take into account also explicit inequality constraints. We compare in simulation the tracking results obtained with a classical algebraic implementation against those derived from the proposed QP implementation taking into account joint torque limits. We consider the classical Cartesian impedance controller and a simplified version, also known as Virtual Model Control. Finally the proposed method was implemented and validated on a humanoid upper-body torque controlled robot. Experimental trials involving various physical interaction conditions were executed to demonstrate the performance of the proposed method.
L1 Patch-Based Image Partitioning into Homogeneous Textured Regions

This paper proposes a novel patch-based variational segmentation method that considers adaptive patches to characterize, in an affine invariant way, the local structure of each homogeneous texture region of the image and thus being capable of grouping the same kind of texture regardless of differences in the point of view or suffered perspective distortion. The patches are computed using an affine covariant structure tensor defined at every pixel of the image domain, so that they can automatically adapt its shape and size. They are used in a segmentation model that uses an L1-norm fidelity term and fuzzy membership functions, which is solved by an alternating scheme. The output of the method is a partition of the image in regions with homogeneous texture together with a patch representative of the texture of each region.
Context Analysis of Scientific Social Networks

This paper presents a solution based on analyses of complex networks in order to address context alignment and context loss between researchers working in projects and possibly in scientific experiments. Network edges with weights indicate the level of alignment between the researchers' contexts. The analyses showed that the context mismatch between members of a project might reveal the reasons why researchers quit their collaborative activities. It was also possible to identify the links that could support the decision of inserting a particular researcher into a project.
Generating Resource and Performance Models for Service Function Chains: The Video Streaming Case

Understanding the behavior of the components of service function chains (SFCs) in different load situations is important for efficient and automatic management and orchestration of services. For this purpose and for practical research in network function virtualization in general, there is a great need for benchmarks and experimental data. In this paper, we describe our experiments for characterizing the relationship between resource demands of virtual network functions (VNFs) and the expected performance of the SFC, considering the individual performance of the VNFs as well as the interdependencies among VNFs within the SFC. We have designed our experiments focusing on video streaming, an important application in this context. We present examples of models for predicting the interdependence between resource demands and performance characteristics of SFCs using support vector regression and polynomial regression models. We also show practical evidence from our experiments that VNFs need to be benchmarked in their final chain setup, rather than individually, to capture important interdependencies that affect their performance. The data gathered from our experiments is publicly available.
On the Performance Analysis of Wifi Based Localization

Currently, WiFi (or IEEE 802.11) enabled infrastructures and devices have become ubiquitous, which makes it promising to provide indoor positioning services via WiFi signals. However, most existing studies on its performance are carried out based on simulations and experiments, and it is still challenging to mathematically characterize the localization error. Therefore, in this paper, the Cramer-Rao lower bound (CRLB) for the localization error by using WiFi signals is established and enables us to carry out a theoretical analysis on the fundamentals of WiFi based localization. Extensive experiments based on the well-known WiFi fingerprint-based localization are then carried out and confirm the correctness of the proposed CRLB model as well as the analysis.
Displacement Amplifier Mechanism for Piezoelectric Actuators Design Using SIMP Topology Optimization Approach

Due to their inherent crystalline properties piezoelectric actuators have a limited deformation. This intrinsic drawback deprives to exploit the potential of these actuators such as, high bandwidth and high resolution in applications that require large displacement range. To overcome this limitation, classical as well as systematic approaches were proposed to design amplification mechanisms. The classical approach leads to empirical mechanisms which are not trivial and needs much experience and intuition. In contrast, systematic approach uses topology optimization method which permits to automatically derive optimal designs that can satisfy specified performances and imposed constraints simultaneously, this with a reasonable time and cost. This paper proposes the design of a mechanism devoted to amplify the displacement of a piezoelectric actuators (PEA). Based on the SIMP topology optimization method, the approach permits to derive a design with a displacement amplification ratio of 4.5, which is higher than with the existing method of Rhombus mechanism. Both finite element (FE) simulation and experimental results confirm and demonstrate the efficiency of the approach.
Classifying atopic dermatitis: protocol for a systematic review of subtypes (phenotypes) and associated characteristics

Introduction Atopic dermatitis is a complex disease with differing clinical presentations. Many attempts have been made to identify uniform subtypes, or phenotypes, of atopic dermatitis in order to identify different aetiologies, improve diagnosis, estimate more accurate clinical prognoses, inform treatment andmanagement or predict treatment efficacy andeffectiveness. However, no consensus yet exists on exactly what defines these phenotypes or how many there are and whether they are genuine or statistical artefacts. This review aims to identify previously reported phenotypes of atopic dermatitis, the features used to define them and any characteristics or clinical outcomes significantly associated with them. Methods and analysis We will search Ovid Embase, Ovid MEDLINE and Web of Science from inception to the latest available date at the time of the search for studies attempting to classify atopic dermatitis in humans using any cross-sectional or longitudinal epidemiological or interventional design. Primary outcomes are atopic dermatitis phenotypes, features used to define them and characteristics associated with them in subsequent analyses. A secondary outcome is the methodological approach used to derive them. Two reviewers will independently screen titles and abstracts for inclusion, extract data and assess study quality. We will present the results of this review descriptively and with frequencies where possible. Ethics and dissemination Ethical approval is not required for this study as it is a systematic review. We will report results from this systematic review in a peer-reviewed journal. The main value of this study will be to inform further research. PROSPERO registration number CRD42018087500
An Adaptive Method to Learn Directive Trust Strength for Trust-aware Recommender Systems

Trust Relationships have shown great potential to improve recommendation quality, especially for cold start and sparse users. Since each user trust their friends in different degrees, there are numbers of works been proposed to take Trust Strength into account for recommender systems. However, these methods ignore the information of trust directions between users. In this paper, we propose a novel method to adaptively learn directive trust strength to improve trust-aware recommender systems. Advancing previous works, we propose to establish direction of trust strength by modeling the implicit relationships between users with roles of trusters and trustees. Specially, under new trust strength with directions, how to compute the directive trust strength is becoming a new challenge. Therefore, we present a novel method to adaptively learn directive trust strengths in a unified framework by enforcing the trust strength into range of [0, 1] through a mapping function. Our experiments on Epinions and Ciao datasets demonstrate that the proposed algorithm can effectively outperform several state-of-art algorithms on both MAE and RMSE metrics.
Detection of Copy-Move Image Forgery Using Local Binary Pattern with Discrete Wavelet Transform and Principle Component Analysis

There has been a wide development in the zone of advanced picture usage. One of the fundamental problems in this present reality is to judge the genuineness of a particular picture. These days it is anything but difficult to alter and manufacture computerized picture with the progression of the capable advanced picture handling programming and simple accessibility of the apparatuses. The most widely recognized type of picture control systems is the district duplication additionally called as duplicate move falsification where a segment of the picture is replicated and glue to another part in the same advanced picture. To examine such legal investigation, different procedures and technique have been created in the past writing. In this paper will utilize productive calculations in light of Local Binary Pattern (LBP) with discrete wavelet transform (DWT) and principle component analysis (PCA). Firstly, change the picture from RGB to YCbCr by applying Pre-processing. Secondly, Discrete Wavelet Transform is applied above the image for compression. Guess sub-picture contains low recurrence parts having most extreme data. LL sub-picture is separated in covering squares. Thirdly Local Binary Pattern is performed. Fourthly principle component analysis utilized to match between chunks as feature matching. The latest step is support vector machine (SVM) classify to choice of which slice is the fake.
Noninvasive neuromodulation techniques for the management of phantom limb pain: a systematic review of randomized controlled trials

Neuromodulation techniques work by modulating pain perception by inducing changes in polarity of the neuronal membrane and thereby cortical excitability. The aim of this review is to evaluate the efficiency and safety of noninvasive neuromodulation techniques for phantom limb pain (PLP). A systematic literature search in the PubMed, Scopus, Web of Science, and Cochrane Library databases was performed to identify studies investigating the effects of noninvasive neuromodulation for PLP. The included journal articles were assessed with Furlan et al.’s method for examining the risk of bias to assess methodologic quality, and evidence was graded using the GRADE approach. The literature search identified 239 studies. Of these 239, four studies fulfilled the inclusion criteria and were included for data extraction. Two of the studies focused on repetitive transcranial magnetic stimulation (rTMS) whereas two other concentrated on transcranial direct current stimulation (tDCS). The present review showed that there is conflicting evidence to support the use of tDCS in short term and moderate evidence to support the use of rTMS in immediate and short term. It is important to recognize that this evidence comes from a very small sample size. No serious adverse effects were reported. Further information from randomized controlled trials with larger sample size investigating immediate and short-term and long-term effects are needed to clarify the best effective stimulation parameters and number of sessions of tDCS and rTMS for PLP.
Cost-Effective and Traffic-Optimal Data Placement Strategy for Cloud-based Online Social Networks

Cloud-based Online Social Networks (OSNs) make it easier for geographically dispersed users to communicate with each other. These users not only demand to quickly access their own data but also hope to access their friends' data with low latency. In order to solve the problem, it is necessary to design a replica placement strategy to manage data on large-scale social networks and reduce the data storage costs while meeting the access latency requirement. In this paper, we propose a novel genetic algorithm-based data placement strategy to find an optimal number of replicas for each user's data and their optimal location. The method can reduce the inter-server traffic load across servers and ensure that users can access data in a tolerable time. Experiments with real Facebook dataset demonstrate that our data placement strategy can significantly reduce the cost of data storage and inter-server traffic.
Destabilization of the von Willebrand factor A2 domain under oxidizing conditions investigated by molecular dynamics simulations

The protein von Willebrand factor (VWF) is key for the adhesion of blood platelets to sites of vascular injury. Recent studies have shown that the release of oxidative agents during inflammation increases the platelet-tethering activity of VWF contributing to a pro-thrombotic state. This has been linked to the oxidation of methionine residues in the A1, A2 and A3 domains of VWF. The A1 domain binds to platelet surface receptors glycoprotein Ib α (GpIbα). This interaction has been shown to be inhibited under static conditions by the neighboring A2 domain. Tensile force exerted by blood flow unfolds the A2 domain normally leading to its cleavage by the metalloprotease ADAMTS13 preventing pathological thrombus formation. However, oxidizing conditions inhibit proteolysis through ADAMTS13. Here, molecular dynamics simulations tested the hypothesis whether methionine oxidation induced by inflammatory conditions favors unfolding of the A2 domain contributing to the experimentally observed activation of VWF. The results indicate that oxidation of methionine residues located near the C-terminal helix of the A2 domain reduce the force necessary to initiate unfolding. Furthermore, oxidation of methionine residues shifts the thermodynamic equilibrium of the A2 domain fold towards the denatured state. This work suggests a mechanism whereby oxidation reduces the kinetic and thermodynamic stability of the A2 domain removing its inhibitory function on the binding of the A1 domain to GpIbα.
Cellular-Level Characterization of Dengue and Zika Virus Infection Using Multiagent Simulation

In this paper we present a computational model aimed at characterizing the Zika viral infection at a cellular level based on measurements done on viral Dulbecco plaques over time, and describe our current state of progress in the modeling task. So far we have developed an agent-based simulation model of the dispersion of the virus on the cells conforming the viral plaque. The growth rate of the viral plaques and the number of cells counted on each plaque were used to characterize the viral infection in terms of parameters related to the fate of infected cells, such as the probability of a cell infecting its neighboring cells and the probability of an infected cell of dying at any given moment. The model can be used to predict viral plaque growth patterns similar to those observed in the laboratory. Our current efforts focus on optimizing the model parameters to fit the experimental data. Further development of the model includes the description of viral infection kinetics of specific viral strains. Our model has been developed using the agent-based modeling language Netlogo [1].
Multiple-Input Neural Network-Based Residual Echo Suppression

A residual echo suppressor (RES) aims to suppress the residual echo in the output of an acoustic echo canceler (AEC). Spectral-based RES approaches typically estimate the magnitude spectra of the near-end speech and the residual echo from a single input, that is either the far-end speech or the echo computed by the AEC, and derive the RES filter coefficients accordingly. These single inputs do not always suffice to discriminate the near-end speech from the remaining echo. In this paper, we propose a neural network-based approach that directly estimates the RES filter coefficients from multiple inputs, including the AEC output, the far-end speech, and/or the echo computed by the AEC. We evaluate our system on real recordings of acoustic echo and near-end speech acquired in various situations with a smart speaker. We compare it to two single-input spectral-based approaches in terms of echo reduction and near-end speech distortion.
Data-driven Construction of Symbolic Process Models for Reinforcement Learning

Reinforcement learning (RL) is a suitable approach for controlling systems with unknown or time-varying dynamics. RL in principle does not require a model of the system, but before it learns an acceptable policy, it needs many unsuccessful trials, which real robots usually cannot withstand. It is well known that RL can be sped up and made safer by using models learned online. In this paper, we propose to use symbolic regression to construct compact, parsimonious models described by analytic equations, which are suitable for realtime robot control. Single node genetic programming (SNGP) is employed as a tool to automatically search for equations fitting the available data. We demonstrate the approach on two benchmark examples: a simulated mobile robot and the pendulum swing-up problem; the latter both in simulations and real-time experiments. The results show that through this approach we can find accurate models even for small batches of training data. Based on the symbolic model found, RL can control the system well.
Network Topology Inference in Swarm Robotics

Swarm robotics refers to the implementation of swarm intelligence features like autonomy and self-organization to a collective of robots. This study focuses on the construction of a topological graph that represents both the magnitude and orientation of swarm interactions. Such structure is used for identifying global parameters like leadership and to derive a relationship between the distribution of interaction magnitudes and swarm parameters. Interaction magnitudes were derived from the trajectory distance between nearest neighbors and it was found that the distribution is able to differentiate between only a small subset of controllers, communication ranges and swarm sizes. Leader detection was based on the analysis of position vectors orientation in local neighborhoods. The method was successful at a 100% rate for 10 and 30 robots, while for 60 a minimum rate of 67% was obtained. Additionally, processing times never exceeded a simulation duration for swarms up to 30 robots, with the potential to parallelize for larger sizes.
Multiplicative vs. Additive Half-Quadratic Minimization for Robust Cost Optimization

It has been experimentally shown in the literature that half-quadratic (HQ) lifting leads to very effective methods for robust cost minimization when combined with a joint optimization strategy. More precisely, the multiplicative formulation of HQ minimization was employed in these works. In this work we address the questions whether the complementary additive form of HQ lifting is beneficial for solving robust estimation problems. Additive HQ minimization is appealing due to its connection with a quadratic relaxation and because it fully pushes the difficulties induced by robust costs to independent terms in the (lifted) cost function. We also propose a "double lifting" method combining additive and multiplicative HQ minimization. We report numerical results for synthetic problems and standard bundle adjustment instances.
Use of second-order sliding mode observer for low-accuracy sensing in hydraulic machines

Low-accuracy sensing is very common for the large hydraulic machines and does not allow for directly measuring the relative velocity which can be, otherwise, required for the control and monitoring purposes. This paper provides a case study of designing the second-order sliding mode observer based on the super-twisting robust exact differentiator. The nominal part of the system dynamics is derived from the simple available system measurements and incorporated into the observer structure. Parasitic by-effects, arising from the sensor sampling, quantization, and non-modeled distortions due to mechanical sensor interface, are shown as the main causes of hampering the final (steady-state) convergence of the observer states. Two cases – a continuous chirp excitation and a sequence of the short square pulses –are demonstrated for the open-loop motion experiments performed on a hydraulic crane, for which an accurate estimation of the motion system states is obtained.