Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the consistency assumptions underlying network meta‐regression using aggregate data

When numerous treatments exist for a disease (Treatments 1, 2, 3, etc), network meta‐regression (NMR) examines whether each relative treatment effect (eg, mean difference for 2 vs 1, 3 vs 1, and 3 vs 2) differs according to a covariate (eg, disease severity). Two consistency assumptions underlie NMR: consistency of the treatment effects at the covariate value 0 and consistency of the regression coefficients for the treatment by covariate interaction. The NMR results may be unreliable when the assumptions do not hold. Furthermore, interactions may exist but are not found because inconsistency of the coefficients is masking them, for example, when the treatment effect increases as the covariate increases using direct evidence but the effect decreases with the increasing covariate using indirect evidence.
SoaAlloc: A Lock-free Hierarchical Bitmap-based Object Allocator for GPUs

Designing dynamic memory allocators for GPUs is challenging because applications can issue allocation requests in a highly parallel fashion and memory access and the data layout must be optimized to achieve good memory bandwidth utilization. Despite recent advances in GPU computing, current memory allocators for SIMD architectures are still not suitable for structured data because they fail to incorporate well-known best practices for optimizing memory access. Therefore, we developed SoaAlloc, a new dynamic object allocator for GPUs. Besides delivering competitive raw (de)allocation performance, SoaAlloc improves the usage of allocated memory with a Structure of Arrays (SOA) data layout and achieves low memory fragmentation through efficient management of free and allocated memory blocks with lock-free, hierarchical bitmaps. The SOA layout alone results in a 2x speedup of application code over state-of-the-art allocators in our benchmarks. Furthermore, SoaAlloc is the first GPU object allocator that provides a do-all operation, which is an important recurring pattern in high-performance code where parallelism is expressed over a set of objects.
Boosted Probabilistic Neural Network for IoT Data Classification

This paper proposes an intelligent and compact machine learning model called an adaboosted semiparametric probabilistic neural network (PNN) to classify IoT data in heterogeneous and dynamic IoT environment. The classified IoT services can be used to prioritize resource allocations to support critical services. The proposed model is applied to the IoT service classification and shows comparable classification accuracy against the other state-of-the-art models but at a reduced computational cost.
A Soft Wearable Robotic Suit for Ankle and Hip Assistance: a Preliminary Study

As a walking assistive device, one of major goals should be reduce wearer's metabolic cost as much as possible. Toward this goal, this paper presents the preliminary study on a new soft wearable robotic suit for ankle and hip assistance for daily life activities. The presented robotic suit is lightweight, and it almost does not restrict the lower limb motion range. In addition, its structure is simple, and thus wearers can easily take the device on and off by themselves. Experimental results on two young subjects show that the robotic suit reduced metabolic cost with averaged reductions of 10.2 % and 6.8 %, respectively, by not drastically disrupting the normal walking biomechanics of the subjects.
An Efficient Movement-Based Handover Prediction Scheme for Hierarchical Mobile IPv6 in VANETs

Wireless mobile communication in vehicular networks is essential for the content delivery of road safety and infotainment services to drivers. However, the vehicles' high mobility and topology changes affect the performance of traditional mobility management protocols over VANETs. Therefore, an efficient mobility management solution that mitigates the challenges of vehicles' mobility is needed. In this paper, we present a predictive hierarchical handover protocol for mobile IP in vehicular networks. We combine the stochastic probability analysis of a hidden Markov model, and the vehicles' movement projection to predict the next handoff. We evaluate the performance of our protocol against different mobile handover protocols using the network simulator NS-2, and various mobility traces. Furthermore, we assess the impact of the different type of observations on the prediction model. Our results showed that our predictive module outperforms all other handover protocols in reducing the handover latency and packet loss.
Coronary Thermodilution Waveforms After Acute Reperfused ST‐Segment–Elevation Myocardial Infarction: Relation to Microvascular Obstruction and Prognosis

Background Invasive measures of microvascular resistance in the culprit coronary artery have potential for risk stratification in acute ST‐segment–elevation myocardial infarction. We aimed to investigate the pathological and prognostic significance of coronary thermodilution waveforms using a diagnostic guidewire. Methods and Results Coronary thermodilution was measured at the end of percutaneous coronary intervention, (PCI) and contrast‐enhanced cardiac magnetic resonance imaging (MRI) was intended on day 2 and 6 months later to assess left ventricular (LV) function and pathology. All‐cause death or first heart failure hospitalization was a pre‐specified outcome (median follow‐up duration 1469 days). Thermodilution recordings underwent core laboratory assessment. A total of 278 patients with acute ST‐segment elevation myocardial infarction EMI (72% male, 59±11 years) had coronary thermodilution measurements classified as narrow unimodal (n=143 [51%]), wide unimodal (n=100 [36%]), or bimodal (n=35 [13%]). Microvascular obstruction and myocardial hemorrhage were associated with the thermodilution waveform pattern (P=0.007 and 0.011, respectively), and both pathologies were more prevalent in patients with a bimodal morphology. On multivariate analysis with baseline characteristics, thermodilution waveform status was a multivariable associate of microvascular obstruction (odds ratio [95% confidence interval]=5.29 [1.73, 16.22];, P=0.004) and myocardial hemorrhage (3.45 [1.16, 10.26]; P=0.026), but the relationship was not significant when index of microvascular resistance (IMR) >40 or change in index of microvascular resistance (5 per unit) was included. However, a bimodal thermodilution waveform was independently associated with all‐cause death and hospitalization for heart failure (odds ratio [95% confidence interval]=2.70 [1.10, 6.63]; P=0.031), independent of index of microvascular resistance>40, ST‐segment resolution, and TIMI (Thrombolysis in Myocardial Infarction) Myocardial Perfusion Grade. Conclusions The thermodilution waveform in the culprit coronary artery is a biomarker of prognosis and may be useful for risk stratification immediately after reperfusion therapy.
Basics of CD8 T-cell immune responses after influenza infection and vaccination with inactivated or live attenuated influenza vaccine

ABSTRACT Introduction: One of the essential mechanisms of virus infection control is cell-mediated cytotoxicity, which can act in an antibody-dependent or -independent fashion and is provided by different effector cells. The role of CD8 T-cells in infection control and in affecting the pathological outcome of different types of infection has been demonstrated in numerous animal studies. Despite this, their role in controlling human influenza infection is not fully understood. Especially, knowledge about their induction and turnover in human influenza infection is limited. Differences in the development of CD8 T-cells after influenza infection or immunizations should be explored in detail, in relation to the bioaccessibility of influenza antigens, site of application and distribution routes. Areas covered: This review focuses on the basics of CD8 T-cell immune response both in human influenza infection and after administration of inactivated or live attenuated influenza vaccine. Some aspects of the accessibility, distribution and presentation of influenza antigens to CD8 T-cells are described. Expert commentary: The CD8 T-cell response is an essential connection between innate and antibody-mediated responses, which are all-important for influenza control. We hypothesize that immunization with live influenza vaccine is the most straightforward artificial way to induce an efficient influenza-specific CD8 T-cell response.
Precise Delineation and Transcriptional Characterization of Bovine Blood Dendritic-Cell and Monocyte Subsets

A clear-cut delineation of bovine bona fide dendritic cells (DC) from monocytes has proved challenging, given the high phenotypic and functional plasticity of these innate immune cells and the marked phenotypic differences between species. Here, we demonstrate that, based on expression of Flt3, CD172a, CD13, and CD4, a precise identification of bovine blood conventional DC type 1 and 2 (cDC1, cDC2), plasmacytoid DC (pDC), and monocytes is possible with cDC1 being Flt3+CD172adimCD13+CD4−, cDC2 being Flt3+CD172a+CD13−CD4−, pDC being Flt3+CD172adimCD13−CD4+, and monocytes being Flt3−CD172ahighCD13−CD4−. The phenotype of these subsets was characterized in further detail, and a subset-specific differential expression of CD2, CD5, CD11b, CD11c, CD14, CD16, CD26, CD62L, CD71, CD163, and CD205 was found. Subset identity was confirmed by transcriptomic analysis and subset-specific transcription of conserved key genes. We also sorted monocyte subsets based on their differential expression of CD14 and CD16. Classical monocytes (CD14+CD16−) clustered clearly apart from the two CD16+ monocyte subsets probably representing intermediate and non-classical monocytes described in human. The transcriptomic data also revealed differential gene transcription for molecules involved in antigen presentation, pathogen sensing, and migration, and therefore gives insights into functional differences between bovine DC and monocyte subsets. The identification of cell-type- and subset-specific gene transcription will assist in the quest for “marker molecules” that—when targeted by flow cytometry—will greatly facilitate research on bovine DC and monocytes. Overall, species comparisons will elucidate basic principles of DC and monocyte biology and will help to translate experimental findings from one species to another.
Schistosoma “Eggs-Iting” the Host: Granuloma Formation and Egg Excretion

Schistosomiasis is a major cause of morbidity in humans invoked by chronic infection with parasitic trematodes of the genus Schistosoma. Schistosomes have a complex life-cycle involving infections of an aquatic snail intermediate host and a definitive mammalian host. In humans, adult male and female worms lie within the vasculature. Here, they propagate and eggs are laid. These eggs must then be released from the host to continue the life cycle. Schistosoma mansoni and Schistosoma japonicum reside in the mesenteric circulation of the intestines with egg excreted in the feces. In contrast, S. haematobium are present in the venus plexus of the bladder, expelling eggs in the urine. In an impressive case of exploitation of the host immune system, this process of Schistosome “eggs-iting” the host is immune dependent. In this article, we review the formation of the egg granuloma and explore how S. mansoni eggs laid in vasculature must usurp immunity to induce regulated inflammation, to facilitate extravasation through the intestinal wall and to be expelled in the feces. We highlight the roles of immune cell populations, stromal factors, and egg secretions in the process of egg excretion to provide a comprehensive overview of the current state of knowledge regarding a vastly unexplored mechanism.
Coronary Artery Segmentation by Deep Learning Neural Networks on Computed Tomographic Coronary Angiographic Images

Coronary artery lumen delineation, to localize and grade stenosis, is an important but tedious and challenging task for coronary heart disease evaluation. Deep learning has recently been successful applied to many applications, including medical imaging. However for small imaged objects such as coronary arteries and their segmentation, it remains a challenge. This paper investigates coronary artery lumen segmentation using 3D U-net convolutional neural networks, and tests its utility with multiple datasets on two settings. We adapted the computed tomography coronary angiography (CTCA) volumes into small patches for the networks and tuned the kernels, layers and the batch size for machine learning. Our experiment involves additional efforts to select and test various data transform, so as to reduce the problem of overfitting. Compared with traditional normalization of data, we showed that subject-specific normalization of dataset was superior to patch based normalization. The results also showed that the proposed deep learning approach outperformed other methods, evaluated by the Dice coefficients.
Reverse dosimetry modeling of toluene exposure concentrations based on biomonitoring levels from the Canadian health measures survey

ABSTRACT Biomonitoring might provide useful estimates of population exposure to environmental chemicals. However, data uncertainties stemming from interindividual variability are common in large population biomonitoring surveys. Physiologically based pharmacokinetic (PBPK) models might be used to account for age- and gender-related variability in internal dose. The objective of this study was to reconstruct air concentrations consistent with blood toluene measures reported in the third Canadian Health Measures Survey using reverse dosimetry PBPK modeling techniques. Population distributions of model’s physiological parameters were described based upon age, weight, and size for four subpopulations (12–19, 20–39, 40–59, and 60–79 years old). Monte Carlo simulations applied to PBPK modeling allowed converting the distributions of venous blood measures of toluene obtained from CHMS into related air levels. Based upon blood levels observed at the 50th, 90th and 95th percentiles, corresponding air toluene concentrations were estimated for teenagers aged 12–19 years as being, respectively, 0.009, 0.04 and 0.06 ppm. Similarly, values were computed for adults aged 20–39 years (0.007, 0.036, and 0.06 ppm), 40–59 years (0.007, 0.036 and 0.06 ppm) and 60–79 years (0.006, 0.022 and 0.04 ppm). These estimations are well below Health Canada’s maximum recommended chronic air guidelines for toluene. In conclusion, PBPK modeling and reverse dosimetry may be combined to help interpret biomonitoring data for chemical exposure in large population surveys and estimate the associated toxicological health risk.
Eliminating Individual Bias to Improve Stress Detection from Multimodal Physiological Data

Stress monitoring is important for mental wellbeing and early detection of related disorders. The current work is focused on stress detection from multiple non-invasive physiological signals like Electroencephalogram (EEG), Photoplethysmogram (PPG) and Galvanic Skin Response (GSR). We show that, compared to using only the well known EEG band powers in different frequencies for stress detection, an early fusion with GSR and PPG features shows a significant improvement. Maximum Relevance Minimum Redundancy (mRMR) based feature selection is used to identify the most suitable physiological features correlating with stress. A major contribution of this work lies in eliminating subject-specific bias to improve the classification accuracy. We use self-reported values of Valence, Arousal and Dominance to cluster subjects and build separate classification models specific to clusters. The proposed approach is validated on a publicly available dataset comprising 146 data instances from 10 subjects. The performances of Leave-One- Subject-Out cross validation (LOSOCV) in terms of mean Fscores are 0.61 using EEG features only, 0.64 using early fusion of EEG, GSR and PPG features and 0.69 by applying our clustering technique before fusion and classification.
Daytime sleepiness affects gait auditory synchronization ability

Sleep disturbances in modern life lead to cognitive and motor performance impairments in everyday tasks such as gait. The most common symptom of these disturbances is daytime sleepiness, which can be assessed by questionnaires such as the Epworth Sleep Scale (ESS). The ESS evaluates sleep health and daytime dysfunction. The goal of this study is to assess the influence of sleepiness on a motorauditory synchrony task, rhythmed gait. High and low sleepiness clusters were formed based on the participants ESS scores. Walking on a treadmill, two different rhythmic auditory stimulus conditions were set with a metronome: isochronous and non-isochronous. Reflective markers on both heels with seven infrared cameras were used to assess the difference between footfall and metronome beep, what is named synchronization error (SE). There was a tendency to anticipate the beep in the HS group when compared to the LS group only in the non-isochronous stimulus condition that was statistically significant. Sleep disturbances that generate daytime sleepiness may bring detrimental effects on brain areas that could be responsible for the real-time adjustment of gait and sustained attention. These impairments may be responsible for the larger synchronization error with larger relative phase of the group with high sleepiness. More studies are necessary involving other parameters of sleep and gait to identify sleep disturbances through gait analysis.
Human White Adipose Tissue Metabolome: Current Perspective

Interest in metabolites produced by adipose tissue has increased substantially in the past several decades. Previously regarded as an inert energy storage depot, adipose tissue is now viewed as a complex metabolically active organ with considerable impact on human health. The emerging field of mass spectrometry‐based metabolomics presents a powerful tool for the study of processes in complex biological matrices including adipose tissue.
Alzheimer'S Disease Classification Using Bag-Of-Words Based On Visual Pattern Of Diffusion Anisotropy For DTI Imaging

Diffusion tensor imaging (DTI) has recently been added to the large scale of studies for Alzheimer's Disease (AD) to investigate the White Matter (WM) defects that are not detectable using structural MRI. In this paper, we extracted Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) features, based on the visual diffusion patterns of Fractional Anisotropy (FA), and Mean Diffusivity (MD) maps, to build bag-of-words AD-signature for the hippocampal area. The experiments were accomplished with a subset of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset formed of AD patients (n = 35), Early Mild Cognitive Impairment (EMCI) (n=6), Late Mild Cognitive Impairment (LMCI) (n=24) and cognitively healthy elderly Normal Controls (NC) (n=31). The preliminary studied experiments give promising results that would consider the proposed system as an accurate and useful tool to capture the AD leanness with accuracy of 87% and 89% for FA and MD maps respectively.
His-Leu, an angiotensin I-derived peptide, does not affect haemodynamics in rats

Introduction: The dipeptide histidine-leucine (His-Leu) is formed in the process of converting angiotensin I into angiotensin II. Several studies show that short peptides containing His-Leu may produce significant haemodynamic effects; however, to the best of our knowledge, data on haemodynamic effects of His-Leu are not available in medical databases. Materials and methods: We evaluated acute haemodynamic effects of intravenous administration of either 0.9% NaCl (vehicle) or His-Leu at a dose of 3–15 mg/kg body weight in anaesthetized 15–16-week-old, male, normotensive Wistar Kyoto and spontaneously hypertensive rats. Chronic effects of treatment with either the vehicle or His-Leu at a dose of 15 mg/kg body weight given subcutaneously daily were determined during continuous telemetry recordings in freely moving rats. Results: In anaesthetized rats both the vehicle and His-Leu produced a mild and transient increase in blood pressure and no change in plasma renin activity. There was no significant difference in haemodynamics between the rats infused with the vehicle and the rats infused with His-Leu. In chronic studies, seven-day treatment with vehicle and with His-Leu did not affect arterial blood pressure in freely moving rats. Conclusion: His-Leu does not produce either acute or chronic changes in arterial blood pressure in normotensive and hypertensive rats.
Artificial Neural Networks and Fuzzy Neural Networks for Solving Civil Engineering Problems

University of Podgorica, Faculty of Civil Engineering, Podgorica, Montenegro Ss. Cyril and Methodius University, Faculty of Civil Engineering, Skopje, Macedonia Brno University of Technology, Faculty of Civil Engineering, Institute of Structural Economics and Management, Brno, Czech Republic Director of the Building Physics & Construction Technology Laboratory, Civil Engineering Department University of Minho, Guimaraes, Portugal Slovak University of Technology in Bratislava, Department of Hydraulic Engineering, Bratislava, Slovakia
Cooperative Moving-Target Enclosing of Networked Vehicles With Constant Linear Velocities

This paper investigates the cooperative moving-target enclosing control problem of networked unicycle-type nonholonomic vehicles with constant linear velocities. The information of the target is only known to some of the vehicles, and the topology of the vehicle network is described by a directed graph. A dynamic control law is proposed to steer the vehicles, such that they can get close to orbiting around the target while the target is moving with a time-vary velocity. Besides, the constraint of bounded angular velocity for the vehicles can always be satisfied. The proposed control law is distributed in the sense that each vehicle only uses its own information and the information of its neighbors in the network. Finally, simulation results of an example validate the effectiveness of the proposed control law.
Wireless Terahertz System Architectures for Networks Beyond 5G

The present white paper focuses on the system requirements of TERRANOVA. Initially details the key use cases for the TERRANOVA technology and presents the description of the network architecture. In more detail, the use cases are classified into two categories, namely backhaul & fronthaul and access and small cell backhaul. The first category refers to fibre extender, point-to-point and redundancy applications, whereas the latter is designed to support backup connection for small and medium-sized enterprises (SMEs), internet of things (IoT) dense environments, data centres, indoor wireless access, ad hoc networks, and last mile access. Then, it provides the networks architecture for the TERRANOVA system as well as the network elements that need to be deployed. The use cases are matched to specific technical scenarios, namely outdoor fixed point-to-point (P2P), outdoor/indoor individual point-to-multipoint (P2MP), and outdoor/indoor "quasi"-omnidirection, and the key performance requirements of each scenario are identified. Likewise, we present the breakthrough novel technology concepts, including the joint design of baseband signal processing for the complete optical and wireless link, the development of broadband and spectrally efficient RF-frontends for frequencies >275 GHz, as well as channel modelling, waveforms, antenna array and multiple-access schemes design, which we are going to use in order to satisfy the presented requirements. Next, an overview of the required new functionalities in both physical (PHY) layer and medium access control (MAC) layers in the TERRANOVA system architecture will be given. Finally, the individual enablers of the TERRANOVA system are combined to develop particular candidate architectures for each of the three technical scenarios.