Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Advances of Edge Cache in Radio Access Networks for Internet of Things: Techniques, Performances, and Challenges

The edge cache is an effective way to reduce the heavy traffic load and the end-to-end latency in radio access networks (RANs) for supporting a number of critical Internet of Things (IoT) services and applications. It has been verified to provide high spectral efficiency (SE), high energy efficiency (EE), and low latency. Along with several key techniques that have been applied, such as device-to-device communication and predictive caching, the edge cache techniques in RANs for IoT are becoming diversified. This paper comprehensively surveys the recent advances of the edge cache in RANs, including the key techniques and the corresponding performances. In particular, the key techniques are presented from the viewpoints of the deployment location of edge caches, content placement strategy, and coded caching. An advanced hierarchical edge cache structure is presented, and the main impacts on SE, EE, and latency of the key techniques are mainly summarized. Several open issues and challenges are identified as well to spur future investigations, in which the joint optimization of radio and cache resources, the edge cache with mobile edge computing and network intelligence, privacy, and security are discussed.
Reaping the Benefits of Dynamic TDD in Massive MIMO

Recent advances in massive multiple-input multiple-output (MIMO) communication show that equipping base stations (BSs) with large antenna arrays can significantly improve the performance of cellular networks. Massive MIMO has the potential to mitigate the interference in the network and enhance the average throughput per user. On the other hand, dynamic time-division duplexing (TDD), which allows neighboring cells to operate with different uplink (UL) and downlink (DL) subframe configurations, is a promising enhancement for the conventional static TDD. Compared with static TDD, dynamic TDD can offer more flexibility to accommodate various UL and DL traffic patterns across different cells, but may result in additional interference among cells transmitting in different directions. Based on the unique characteristics and properties of massive MIMO and dynamic TDD, we propose a marriage of these two techniques, i.e., to have massive MIMO address the limitation of dynamic TDD in macrocell (MC) networks. Specifically, we advocate that the benefits of dynamic TDD can be fully extracted in MC networks equipped with massive MIMO, i.e., the BS-to-BS interference can be effectively removed by increasing the number of BS antennas. We provide detailed analysis using random matrix theory to show that the effect of the BS-to-BS interference on UL transmissions vanishes as the number of BS antennas per user grows infinitely large. Last but not least, we validate our analysis by numerical simulations.
Machine learning based analysis of cardiovascular images

Cardiovascular diseases (CVDs), including coronary artery disease (CAD) and congenital heart disease (CHD) are the global leading cause of death. Computed tomography (CT) and magnetic resonance imaging (MRI) allow non-invasive imaging of cardiovascular structures. This thesis presents machine learning methods for analysis of CT images of patients with coronary artery disease and MR images of patients with congenital heart disease. To identify CAD patients with increased cardiovascular risk, methods for automatic coronary artery calcium (CAC) scoring in cardiac CT are presented. A method is described that automatically identifies CAC lesions in non-contrast-enhanced CT. Candidate lesions can be selected for expert review based on classifier uncertainty, allowing both accurate and fast CAC scoring. This method and other (semi-)automatic methods for CAC scoring are evaluated in a standardized framework. This evaluation shows that automatic patient CVD risk categorization is feasible, but that CAC lesions at ambiguous locations such as the coronary ostia remain challenging. Furthermore, a deep learning-based method for automatic CAC scoring in contrast-enhanced cardiac CT is described. This method uses a pair of convolutional neural networks (CNNs). Automatically determined calcium scores showed a strong correlation with manual calcium scores in non-contrast-enhanced CT. This might obviate the need for a dedicated non-contrast CT scan for CAC scoring, and thus reduce the CT radiation dose received by patients. To further allow potential CT radiation dose reduction, a CNN is proposed that transforms low-dose CT images into routine-dose CT images. To encourage the CNN to generate realistic images, feedback from an adversarial CNN is used. The results show that the images produced are similar to routine-dose images and that noise reduction by the CNN allows CAC quantification in low-dose patient cardiac CT images in which calcium scoring was otherwise infeasible. In addition, methods are proposed for coronary centerline extraction and coronary artery segmentation in contrast-enhanced cardiac CT. In contrast to previously published methods, these methods do not use predefined vesselness filters. Instead, CNNs predict the direction and radius of coronary arteries or the probability that a voxel lies in a coronary artery, based only on CT values. Moreover, experiments show that a single CNN can be trained to not only perform coronary artery segmentation in cardiac CT, but also brain tissue segmentation in brain MRI and pectoral muscle segmentation in breast MRI. This may lead more general systems in which tasks in different imaging modalities are combined. Finally, to analyze cardiac MR images of patients with congenital heart disease, a CNN is presented for segmentation of the myocardium and blood pool. The network uses dilated convolution kernels, which provide high-resolution feature maps, and reduce the number of parameters in the network. The method achieved top-ranking results among evaluated automatic methods in a public challenge.
The Absence of Toll-Like Receptor 4 Mildly Affects the Structure and Function in the Adult Mouse Retina

The innate immune Toll-like receptor (TLR) family plays essential roles in cell proliferation, survival and function of the central nervous system. However, the way in which TLRs contribute to the development and maintenance of proper retinal structure and function remains uncertain. In this work, we assess the effect of genetic TLR4 deletion on the morphology and function of the retina in mice. Visual acuity and retinal responsiveness were evaluated in TLR4 knockout and wild type C57BL/6J control mice by means of an optomotor test and electroretinography, respectively, from P20 to P360. Retinal structure was also analyzed in both strains using confocal and electron microscopy. ERG data showed impaired retinal responsiveness in TLR4 KO mice, in comparison to wild type animals. The amplitudes of the scotopic a-waves were less pronounced in TLR4-deficient mice than in wild-type animals from P30 to P360, and TLR4 KO mice presented scotopic b-wave amplitudes smaller than those of age-matched control mice at all ages studied (P20 to P360). Visual acuity was also relatively poorer in TLR4 KO as compared to C57BL/6J mice from P20 to P360, with significant differences at P30 and P60. Immunohistochemical analysis of retinal vertical sections showed no differences between TLR4 KO and C57BL/6J mice, in terms of either photoreceptor number or photoreceptor structure. Horizontal cells also demonstrated no morphological differences between TLR4 KO and wild-type mice. However, TLR4 KO mice exhibited a lower density of bipolar cells (15% less at P30) and thus fewer bipolar cell dendrites than the wild type control mouse, even though both confocal and electron microscopy images showed no morphologic abnormalities in the synaptic contacts between the photoreceptors and second order neurons. Microglial cell density was significantly lower (26% less at P30) in TLR4 KO mice as compared to wild-type control mice. These results suggest that TLR4 deletion causes functional alterations in terms of visual response and acuity, probably through the loss of bipolar cells and microglia, but this receptor is not essential for the processing of visual information in the retina.
Privacy-Preserving Similar Patient Queries for Combined Biomedical Data

Abstract The decreasing costs of molecular profiling have fueled the biomedical research community with a plethora of new types of biomedical data, enabling a breakthrough towards more precise and personalized medicine. Naturally, the increasing availability of data also enables physicians to compare patients’ data and treatments easily and to find similar patients in order to propose the optimal therapy. Such similar patient queries (SPQs) are of utmost importance to medical practice and will be relied upon in future health information exchange systems. While privacy-preserving solutions have been previously studied, those are limited to genomic data, ignoring the different newly available types of biomedical data. In this paper, we propose new cryptographic techniques for finding similar patients in a privacy-preserving manner with various types of biomedical data, including genomic, epigenomic and transcriptomic data as well as their combination. We design protocols for two of the most common similarity metrics in biomedicine: the Euclidean distance and Pearson correlation coefficient. Moreover, unlike previous approaches, we account for the fact that certain locations contribute differently to a given disease or phenotype by allowing to limit the query to the relevant locations and to assign them different weights. Our protocols are specifically designed to be highly efficient in terms of communication and bandwidth, requiring only one or two rounds of communication and thus enabling scalable parallel queries. We rigorously prove our protocols to be secure based on cryptographic games and instantiate our technique with three of the most important types of biomedical data – namely DNA, microRNA expression, and DNA methylation. Our experimental results show that our protocols can compute a similarity query over a typical number of positions against a database of 1,000 patients in a few seconds. Finally, we propose and formalize strategies to mitigate the threat of malicious users or hospitals.
Investigating Gene–Gene and Gene–Environment Interactions in the Association Between Overnutrition and Obesity-Related Phenotypes

Introduction: Animal studies suggested that NFKB1, IKBKB, and SOCS3 genes could be involved in the association between overnutrition and obesity. This study aims to investigate interactions involving these genes and macronutrient intakes affecting obesity-related phenotypes. Methods: We used a traditional statistical method, logistic regression, and compared it to alternative statistical method, multifactor dimensionality reduction (MDR) and penalized logistic regression (PLR), to better detect genes/environment interactions in the Toronto Nutrigenomics and Health Study (n = 1639) using dichotomized body mass index (BMI) and waist circumference as obesity-related phenotypes. Exposure variables included genotype on 54 single nucleotide polymorphisms (NFKB1: 18, IKBKB: 9, SOCS3: 27), macronutrient (carbohydrates, protein, fat) and alcohol intakes and ethno-cultural background. Results: After correction for multiple testing, no interaction was found using logistic regression. MDR identified interactions between SOCS3 rs6501199 and rs4969172, and IKBKB rs3747811 affecting BMI in the Caucasian population; SOCS3 rs6501199 and NFKB1 rs1609798 affecting WC in the Caucasian population; and SOCS3 rs4436839 and IKBKB rs3747811 affecting WC in the South Asian population. PLR found a main effect of SOCS3 rs12944581 on BMI among the South Asian population. Conclusion: While MDR and PLR had discordant results, some models support results from previous studies. These results emphasize the need to use alternative statistical methods to investigate high-order interactions and suggest that variants in the nutrient-responsive hypothalamic IKKB/NF-kB signaling pathway may be involved in obesity pathogenesis.
Blocking α2δ-1 Subunit Reduces Bladder Hypersensitivity and Inflammation in a Cystitis Mouse Model by Decreasing NF-kB Pathway Activation

Bladder pain is frequently associated with bladder inflammation, as in conditions like interstitial cystitis (IC), for which current analgesic therapies have limited efficacy. The antinociceptive effect of alpha-2-delta (α2δ) ligands on inflammation-associated visceral pain like that experienced in cystitis has been poorly investigated. To investigate the effect of pregabalin (PGB), an α2δ ligand, we evaluated its impact on mechanical hyperalgesia in a mouse model of cystitis induced by cyclophosphamide (CYP). We further studied its effect on inflammation and NF-kB pathway activation. Acute cystitis was induced by intraperitoneal injection of 150 mg kg-1 of CYP in C57Bl/6J male mice. PGB was subcutaneously injected (30 mg kg-1) 3 h after CYP injection. The effect of PGB on CYP-induced mechanical referred hyperalgesia (abdominal Von Frey test), inflammation (organ weight, cytokine production, α2δ subunit level, NF-kB pathway activation) were assessed 1 h after its injection. In parallel, its effect on cytokine production, α2δ subunit level and NF-kB pathway activation was assessed in vitro on peritoneal exudate cells (PECs) stimulated with LPS. PGB treatment decreased mechanical referred hyperalgesia. Interestingly, it had an anti-inflammatory effect in the cystitis model by reducing pro-inflammatory cytokine production. PGB also inhibited NF-kB pathway activation in the cystitis model and in macrophages stimulated with LPS, in which it blocked the increase in intracellular calcium. This study shows the efficacy of PGB in hypersensitivity and inflammation associated with cystitis. It is therefore of great interest in assessing the benefit of α2δ ligands in patients suffering from cystitis.
Two Time-Scale Resource Management for Green Internet of Things Networks

It is expected that billions of objects will be connected through sensors and embedded devices for pervasive intelligence in the coming era of Internet of Things (IoT). However, the performance of such ubiquitous interconnection highly depends on the supply of network resources in terms of both energy and spectrum. Librating IoT devices from the resource deficiency, we consider a green IoT network in which the IoT devices transmit data to a fusion node over multihop relaying. To achieve sustainable operation, IoT devices obtain energy from both ambient energy sources and power grid, while opportunistically access the licensed spectrum for data transmission. We formulate a stochastic problem to optimize the network utility minus the cost on on-grid energy purchasing. The problem formulation takes into account the different granularity in the changing of harvested energy, power price, and primary user activities. To address the problem, we propose a Lyapunov-based framework to decompose the problem into different time scales, based on which an online two time-scale resource allocation algorithm, is developed which determines the harvested and purchased energy in a large time scale, and the channel allocation and data collection in a small time scale. Furthermore, we analyze the required data buffer and energy buffer to support the proposed algorithm. Extensive simulation results validate the correctness of the analysis and the efficiency of the proposed algorithm.
Development and Comparison of Two Multiresidue Methods for the Determination of 17 Aspergillus and Fusarium Mycotoxins in Cereals Using HPLC-ESI-TQ-MS/MS

Cereals can be contaminated by several mycotoxins, whose co-presence may represent an undervalued risk for humans and animals. Maize and wheat are the most contaminated cereals and in temperate areas could be affected in field conditions by several Fusarium and Aspergillus infections. To date, only B-fumonisins (FBs), aflatoxins (AFs), zearalenone (ZEA), deoxynivalenol (DON) and T-2 and HT-2 toxins have been regulated in cereals in European Union. The other fungal metabolites, are commonly referred to as “emerging” and “masked” mycotoxins, and more information on their occurrence in combination with the regulated mycotoxins, are needed to design combined toxicological and exposure assessments. This research intends to develop and compare two multiresidue HPLC-ESI-TQ-MS/MS methods for the simultaneous determination of the main regulated, emerging and masked mycotoxins in maize and wheat, among which: FB1, FB2, DON, ZEA, AFB1, AFB2, AFG1, AFG2, moniliformin (MON), deoxynivalenol-3-glucoside (DON-3-G), 3-acetyldeoxynivalenol (3-ADON), 15-acetyldeoxynivalenol (15-ADON), nivalenol (NIV), enniatins A, A1, B, B1 (ENNA, ENNA1, ENNB, ENNB1). The extraction was performed for both methods using a mixture of CH3CN/H2O/CH3COOH (79/20/1, v/v/v), while the dilution/purification was carried out through two different procedures: (1) by the “dilute-and-shoot” technique diluting 1:2 the filtered extract with CH3CN/H2O/CH3COOH (20/79/1, v/v/v) to reduce the matrix effect; (2) using the Oasis® PRiME HLB clean-up columns. The analysis was carried out using CH3OH and H2O both acidified with 0.1% of CH3COOH as eluents. The injection volume was 20 μL and the flow rate 200 μL min-1. The analysis of two reference material (maize and wheat), was performed to evaluate the trueness and precision of the two methods by matrix-matched calibration curves. For all the regulated mycotoxins analyzed by both methods, the range of recovery percentage established by the Regulation (EC) No. 401/2006 was respected, except for ZEA by using the Oasis® PRiME HLB clean-up columns. Nevertheless, the results suggest that the Oasis® PRiME HLB clean-up columns, could be a valid alternative to the dilute-and-shoot method, although an additional cost for the clean-up has to be considered. In conclusion, both two analytical methods considerably reduce the analytical time and costs and therefore result to be promising and applicable for high-throughput routine multi-mycotoxins analysis by the use of a TQ.
Linear and Nonlinear Relationships on E-Repository Adoption

This study investigates the linear and nonlinear relationships among the concerned variables in electronic repository adoption. We apply the polynomial regression and response surface analysis to explain the nonlinear relationships. The results show the perceived behavioral control which is a critical part of the planned behavior theory is not a significant predictor to electronic library adoption, while the subjective norms and attitude are the two significant predictors. Furthermore, the nonlinear relationship between subjective norms and attitude is identified. It is a negative synergy between subjective norms and attitude in E-repository adoption where users have volitional control over their use of the system. Increasing in subjective norms has a decreasing marginal impact on usage intention when the attitude is high; while increasing in attitude has a decreasing marginal impact on usage intention when the subjective norms are high.
Soy Isoflavone Extract Does Not Increase the Intoxicating Effects of Acute Alcohol Ingestion in Human Volunteers

Soy beans contain isoflavones, including daidzein and genistein, with biological activities related to therapeutic effects in reducing osteoporosis, decreasing adverse menopausal manifestations, providing protection from cardiovascular diseases, and reducing hormone-dependent cancers and age-related cognitive-decline. Daidzein has been described as inhibiting the aldehyde-dehydrogenase-2 enzyme (ALDH2), and reducing alcohol use in clinical pilot studies. Our aim was to evaluate the possible interactions between a soy extract product and alcohol in a crossover, single blind, randomized study. Ten healthy male volunteers participated in two experimental sessions: one with a single dose of alcohol (0.5 g/kg, Vodka Absolut, Sweden), and the other with four capsules of a soy extract product (Super-Absorbable Soy Isoflavones, Life-Extension, United States) and, 2 h later, the same dose of alcohol. Results showed no differences in vital signs except a slightly higher significative reduction in diastolic blood pressure at 2, 3, 4, and 8 h after administration with alcohol alone in comparison with soy extract+alcohol. Ethanol-induced subjective and adverse effects were similar for both conditions with the exception of headache (higher at 8 h after alcohol alone). Our results demonstrate that a single dose of a soy isoflavone extract did not influence alcohol pharmacokinetics and pharmacological effects and did not induce any disulfiram-reaction symptoms. Soy extract and alcohol did not interact and can be administered safely.
Frequency-Following Responses to Complex Tones at Different Frequencies Reflect Different Source Configurations

The neural generators of the frequency-following response (FFR), a neural response widely used to study the human auditory system, remain unclear. There is evidence that the balance between cortical and subcortical contributions to the FFR varies with stimulus frequency. In this study, we tried to clarify whether this variation extended to subcortical nuclei at higher stimulus frequencies where cortical sources were inactive. We evoked FFRs, in 17 human listeners with normal hearing (9 female), with three complex tones with missing-fundamentals corresponding to musical tones C4 (262 Hz), E4 (330 Hz), and G4 (393 Hz) presented to left, right, or both ears. Source imaging results confirmed the dominance of subcortical activity underlying both fundamental frequency (F0) and second harmonic (H2) components of the FFR. Importantly, several FFR features (spatial complexity, scalp distributions of spectral strength and inter-trial phase coherence, and functional connectivity patterns) varied systematically with stimulus F0, suggesting an unfixed source configuration. We speculated that the variation of FFR source configuration with stimulus frequency resulted from changing relative contributions of subcortical nuclei. Supportively, topographic comparison between the FFR and the auditory brainstem response (ABR) evoked by clicks revealed that the topography of the F0 component resembled that of the click-ABR at an earlier latency when stimulus F0 was higher and that the topography of the H2 component resembled that of the click-ABR at a nearly fixed latency regardless of stimulus F0, particularly for binaurally evoked FFRs. Possible generation sites of the FFR and implications for future studies were discussed.
Low-rank structured sparse representation and reduced dictionary learning-based abnormity detection

A novel abnormity detection method is presented which combines the low-rank structured sparse representation and reduced dictionary learning. The multi-scale three-dimensional gradient is used as low-level feature by encoding the spatiotemporal information. A group of reduced sparse dictionaries is learnt by low-rank approximation based on the structured sparsity property of the video sequence. The contribution of this study is three-fold: (i) the normal feature clusters can be represented effectively by the reduced dictionaries which are learnt based on the low-rank nature of the data; (ii) the size of dictionary is determined adaptively by the sparse learning method according to the scene, which makes the representation more compact and efficient; and (iii) the proposed abnormity detection method is of low time complexity and real-time detection can be obtained. The authors have evaluated the proposed method against the state-of-the-art methods on the public datasets and very promising results have been achieved.