Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure Dependent-Immunomodulation by Sugar Beet Arabinans via a SYK Tyrosine Kinase-Dependent Signaling Pathway

There is much interest in the immunomodulatory properties of dietary fibers but their activity may be influenced by contamination with microbial-associated molecular patterns (MAMPs) such as lipopolysaccharide (LPS) and lipoteichoic acids, which are difficult to remove completely from biological samples. Bone marrow-derived dendritic cells (BMDCs) from TLR2x4 double-KO mice were shown to be a reliable approach to analyse the immunomodulatory properties of a diverse range of dietary fibers, by avoiding immune cell activation due to contaminating MAMPs. Several of the 44 tested dietary fiber preparations induced cytokine responses in BMDCs from TLR2x4 double-KO mice. The particulate fractions of linear arabinan (LA) and branched arabinan (BA) from sugar beet pectin were shown to be strongly immune stimulatory with LA being more immune stimulatory than BA. Enzymatic debranching of BA increased its immune stimulatory activity, possibly due to increased particle formation by the alignment of debranched linear arabinan. Mechanistic studies showed that the immunostimulatory activity of LA and BA was independent of the Dectin-1 recognition but Syk kinase-dependent.
Design of High-Speed PPM Signal Synchronization Based on FPGA

With the maturity of laser technology and space optical technology, laser communication has become the trend of development. As a modulation method of laser communication, pulse position modulation (PPM) has the characteristics of high power utilization and strong anti-interference ability, which is especially suitable for space applications with precious resources. In this paper, based on FPGA, the high-speed PPM signal is captured and tracked to achieve the synchronization of the signal. In this paper, a parallel sliding correlation algorithm is used to realize acquisition of the PPM signal quickly, and the digital phase locked loop is used to synchronize the signal accurately, which makes the tracking error achieve le-7 level.
Enhancing Video Rate Adaptation With Mobile Edge Computing and Caching in Software-Defined Mobile Networks

Recent advances in software-defined mobile networks (SDMNs), in-network caching, and mobile edge computing (MEC) can have significant effects on video services in next generation mobile networks. In this paper, we jointly consider SDMNs, in-network caching, and MEC to enhance the video service in next generation mobile networks. We use a new video experience evaluation standard called U-video mean opinion score (vMOS), which is a more advanced measurement of the video quality based on the well-known vMOS. With the objective of maximizing the mean U-vMOS, an optimization problem is formulated. Due to the coupling of video data rate, computing resource, and traffic engineering (bandwidth provisioning and paths selection), the problem becomes intractable in practice. Thus, we utilize a dual-decomposition method to decouple those three sets of variables. By this decoupling, video rate adaptation is performed at users with network assistants. End nodes can schedule computing resource independently. Traffic engineering is performed by the software-defined networking controller and base stations. Furthermore, to address the challenges of dynamic change of network status and the drawbacks caused by the frequent exchange of information, we design a decentralized algorithm based on alternating direction method of multipliers to solve the traffic engineering problem. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.
Cybersecurity In Microgrids

The following paper proposes a security scheme applied to microgrids, using symmetric and asymmetric encryption algorithms, digital signatures, hash functions, and SSL certificates to comply with confidentiality, authentication, and integrity aspects in a grid implemented with Raspberry Pi 3 nodes. The study presents the evaluation of different encryption modes to establish the final parameters in the construction of a security scheme. The results showed that the OFB encryption mode in AES is the most suitable for the length of common data in the transfer of information within such networks. In addition, the comparison between the symmetric encryption algorithm AES and the asymmetric encryption algorithm RSA showed that AES has significantly more efficient performance over RSA. Finally, the conclusions and future work are presented.
K+ Efflux Antiporters 4, 5, and 6 Mediate pH and K+ Homeostasis in Endomembrane Compartments1

Arabidopsis thaliana KEA4, KEA5, and KEA6 are endosomal K+ transporters that function in maintaining pH and ion homeostasis in the endomembrane network. KEA4, KEA5, and KEA6 are members of the Arabidopsis (Arabidopsis thaliana) K+ efflux antiporter (KEA) family that share high sequence similarity but whose function remains unknown. Here, we show their gene expression pattern, subcellular localization, and physiological function in Arabidopsis. KEA4, KEA5, and KEA6 had similar tissue expression patterns, and the three KEA proteins localized to the Golgi, the trans-Golgi network, and the prevacuolar compartment/multivesicular bodies, suggesting overlapping roles of these proteins in the endomembrane system. Phenotypic analyses of single, double, and triple mutants confirmed functional redundancy. The triple mutant kea4 kea5 kea6 had small rosettes, short seedlings, and was sensitive to low K+ availability and to the sodicity imposed by high salinity. Also, the kea4 kea5 kea6 mutant plants had a reduced luminal pH in the Golgi, trans-Golgi network, prevacuolar compartment, and vacuole, in accordance with the K/H exchange activity of KEA proteins. Genetic analysis indicated that KEA4, KEA5, and KEA6 as well as endosomal Na+/H+exchanger5 (NHX5) and NHX6 acted coordinately to facilitate endosomal pH homeostasis and salt tolerance. Neither cancelling nor overexpressing the vacuolar antiporters NHX1 and NHX2 in the kea4 kea5 kea6 mutant background altered the salt-sensitive phenotype. The NHX1 and NHX2 proteins in the kea4 kea5 kea6 mutant background could not suppress the acidity of the endomembrane system but brought the vacuolar pH close to wild-type values. Together, these data signify that KEA4, KEA5, and KEA6 are endosomal K+ transporters functioning in maintaining pH and ion homeostasis in the endomembrane network.
Crises and Turnaround Management: Lessons Learned from Recovery of New Orleans and Tulane University Following Hurricane Katrina

By their very nature both man-made and natural disasters are unpredictable, and so we recommend that all health-care institutions be prepared. In this paper, the authors describe and make a number of recommendations, regarding the importance of crisis and turnaround management using as a model the New Orleans public health system and Tulane University Medical School post-Hurricane Katrina. Leadership skills, articulation of vision, nimble decision making, and teamwork are all crucial elements of a successful recovery from disaster. The leadership team demonstrated courage, integrity, entrepreneurship, and vision. As a result, it led to a different approach to public health and the introduction of new and innovative medical education and research programs.
Enhanced Magnetic Nanoparticle Detection Sensitivity in Non-Linear Magnetoimpedance-Based Sensor

The capacity to detect magnetic nanoparticles (MNPs) of a sensor based on non-linear giant magnetoimpedance effect has been addressed. A meander type sensor was designed with four nearly zero magnetostrictive ribbons connected in series. Different amounts of MNPs, in aqueous solutions, were cumulatively added to the sensor surface. The associated evolution of magnetoinductive voltage of first (<inline-formula> <tex-math notation="LaTeX">$V_{Z}$ </tex-math></inline-formula>) and second harmonic (<inline-formula> <tex-math notation="LaTeX">$V_{2f}$ </tex-math></inline-formula>) was analyzed as a function of external magnetic field <inline-formula> <tex-math notation="LaTeX">$H $ </tex-math></inline-formula>(maximum value, <inline-formula> <tex-math notation="LaTeX">$H_{\text {MAX}} = 4.4$ </tex-math></inline-formula> kA/m) and as a function of exciting frequency, <inline-formula> <tex-math notation="LaTeX">$f $ </tex-math></inline-formula>(100 kHz–3 MHz). Both harmonics exhibited optimal detection ratios at <inline-formula> <tex-math notation="LaTeX">$H_{\text {MAX}}$ </tex-math></inline-formula> due to the effect of MNPs stray fields on the transverse permeability of the sensing ribbons. Further analysis with frequency was performed to quantify the amount of MNPs. Different parameters were respectively defined, <inline-formula> <tex-math notation="LaTeX">$m_{\text {LF}} $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${m}_{\text {HF}}^{2}$ </tex-math></inline-formula> (proportional to permeability) and <inline-formula> <tex-math notation="LaTeX">$R_{0}$ </tex-math></inline-formula> revealing the highest sensitivity of second harmonic for the detection of MNPs. In order to further confirm the role of stray fields in MNPs detection, a comparative study was performed employing diamagnetic gold nanoparticles.
Addressing the Language Binding Problem With Dynamic Functional Connectivity During Meaningful Spoken Language Comprehension

During speech, how does the brain integrate information processed on different timescales and in separate brain areas so we can understand what is said? This is the language binding problem. Dynamic functional connectivity (brief periods of synchronization in the phase of EEG oscillations) may provide some answers. Here we investigate time and frequency characteristics of oscillatory power and phase synchrony (dynamic functional connectivity) during speech comprehension. Twenty adults listened to meaningful English sentences and non-sensical “Jabberwocky” sentences in which pseudo-words replaced all content words, while EEG was recorded. Results showed greater oscillatory power and global connectivity strength (mean phase lag index) in the gamma frequency range (30–80 Hz) for English compared to Jabberwocky. Increased power and connectivity relative to baseline was also seen in the theta frequency range (4–7 Hz), but was similar for English and Jabberwocky. High-frequency gamma oscillations may reflect a mechanism by which the brain transfers and integrates linguistic information so we can extract meaning and understand what is said. Slower frequency theta oscillations may support domain-general processing of the rhythmic features of speech. Our findings suggest that constructing a meaningful representation of speech involves dynamic interactions among distributed brain regions that communicate through frequency-specific functional networks.
Developing Machine Learning Products Better and Faster at Startups

There are many uncertainties in developing machine-learning (ML)-based products. Due to the gap between research and development, the overall progress becomes slow, and experiences many failures and learnings only to see an initial idea not working or generating no significant revenue. To minimize these drastic effects, there are continuing studies to make a balanced handshake between research and development to shorten the time span of ML-based products from idea generation to deployment. This paper demonstrates a three-phase ML product development workflow at OneClass. The workflow is a combination of multiple best-practices in the innovation-based startup industry. The first phase of the workflow considers the pivotal idea generation for products that involves data reliability assessment, idea prioritization, expectation setting, and building trust among users. The second phase concentrates on several state-of-the-art strategies for planning and future re-use of several product components. Finally, the actual research and development phase describes the fail-fast method practiced by OneClass to learn quickly from failures and act accordingly. The workflow is followed by the company to develop many sophisticated ML-based products successfully within a very short period of time.
Online Data Organizer: Micro-Video Categorization by Structure-Guided Multimodal Dictionary Learning

Micro-videos have rapidly become one of the most dominant trends in the era of social media. Accordingly, how to organize them draws our attention. Distinct from the traditional long videos that would have multi-site scenes and tolerate the hysteresis, a micro-video: 1) usually records contents at one specific venue within a few seconds. The venues are structured hierarchically regarding their category granularity. This motivates us to organize the micro-videos via their venue structure. 2) timely circulates over social networks. Thus, the timeliness of micro-videos desires effective online processing. However, only 1.22% of micro-videos are labeled with venue information when uploaded at the mobile end. To address this problem, we present a framework to organize the micro-videos online. In particular, we first build a structure-guided multi-modal dictionary learning model to learn the concept-level micro-video representation by jointly considering their venue structure and modality relatedness. We then develop an online learning algorithm to incrementally and efficiently strengthen our model, as well as categorize the micro-videos into a tree structure. Extensive experiments on a real-world data set validate our model well. In addition, we have released the codes to facilitate the research in the community.
Input Filter Optimisation for a DTC Driven Matrix Converter-Fed PMSM Drive

This paper presents optimisation results of input filter parameters for a Direct Torque Control driven Matrix Converter-fed PMSM drive. The main objective of performed research was to achieve better quality indicators of input filter as compared to the analytic method. DTC method is based on controlling the length and angle of a stator flux linkage vector with MC states chosen from a Switching Table. Therefore, presented method has a variable switching frequency, which makes it difficult to analytically appoint a suitable filter. The optimization was performed using one of the newest metaheuristic algorithms - Cuckoo Search. In the article author proposes an objective function, optimization restrictions and filter quality indicators connected with the power factor, electromagnetic torque error, current drawn from the grid, voltage THD and grid current. As a result of this analysis, best filters were chosen, whose performances were then compared in simulation research. Eventually, a filter appointed by mentioned optimization achieved better results for power factor and grid current distortion indicators. Remaining parameters were similar to those of analytic filter.
Magnetic Design and Experimental Evaluation of a Commercially Available Single Integrated Transformer in Three-Phase LLC Resonant Converter

Multiphase topologies are preferably employed in power conversion systems to lessen the per-phase circuit current, conduction losses, devices thermal stresses, and to reduce the output current ripples. A multiphase LLC resonant dc/dc converter usually possesses a number of magnetic cores equal to the number of phases. These magnetic cores are the major contributors to supply volume, weight, and size. For these reasons, circuit designers tend to select the topologies that have a minimal number of magnetic cores. In this paper, the authors aim to promote the industrial applications of the three-phase LLC resonant converter by integrating three transformers into a single commercially available magnetic core to reduce the volume, weight, and cost of the power converter. A comprehensive magnetic analysis for the three-phase-integrated transformer is conducted. Finite element method (FEM) simulation and experimental tests are carried out to validate the proper operation of the integrated transformer utilized in a 390-/12-V to 500-W prototype. Furthermore, the power losses distribution has been presented. The proposed integrated transformer has been proven effective, and it realized a uniform thermal distribution along the core compared to the three-discrete transformers.
DeepLink: A Deep Learning Approach for User Identity Linkage

The typical aim of User Identity Linkage (UIL) is to detect when users from across different social platforms are actually one and the same individual. Existing efforts to address this problem of practical relevance span from user-profile-based, through user-generated-content-based, user-behavior-based approaches to supervised or unsupervised learning frameworks, to subspace learning-based models. Most of them often require extraction of relevant features (e.g., profile, location, biography, networks, behavior, etc.) to model the user consistently across different social networks. However, these features are mainly derived based on prior knowledge and may vary for different platforms and applications. Inspired by the recent successes of deep learning in different tasks, especially in automatic feature extraction and representation, we propose a deep neural network based algorithm for UIL, called DeepLink. It is a novel end-to-end approach in a semi-supervised learning manner, without involving any hand-crafting features. Specifically, DeepLink samples the networks and learns to encode network nodes into vector representation to capture local and global network structures which, in turn, can be used to align anchor nodes through deep neural networks. A dual learning based paradigm is exploited to learn how to transfer knowledge and update the linkage using the policy gradient method. Experiments conducted on several public datasets show that DeepLink outperforms the state-of-the-art methods in terms of both linking precision and identity-match ranking.
Approximating the k-Minimum Distance Rumor Source Detection in Online Social Networks

Online Social networks (OSNs) are now one of the main resources for people to keep abreast of current news and to exchange opinions about new products and social trends, etc. However, unethical use of OSNs also provides a convenient conduit to the diffusion of malicious rumors and misinformation, thus it is of significant importance to discover rumor diffusion and detect the rumor source. This is a very challenging task, as shown in many existing works, e.g., even in the regular tree graphs, the accuracy of detecting the information source from a diffusion snapshot cannot exceed 31%. To overcome this issue, in this work, we propose a novel system framework for information source detection in OSNs and investigate a new rumor source detection problem, called $k$-Minimum Distance Rumor Source Detection (k-MDRSD). Specifically, given a rumor spreading snapshot, our target is to find a small set of rumor candidates which can be used as initial seeds for further iterative query or investigation. To this end, we introduce a notion, called distance error, for rumor candidate sets and formulate the k-MDRSD problem. Resorting to methods from Combinatorics, we develop a near optimal algorithm for k-MDRSD. By experimental simulation, we show that the proposed k-MDRSD significantly improves the likelihood of detecting rumor sources or trend-setters in OSNs.
Successive Amplify-and-Forward Relaying With Network Interference Cancellation

Successive relaying holds the promise of recovering the multiplexing loss due to the half-duplex constraint, by allowing the source and relays to transmit messages simultaneously, but it may cause severe inter-relay interference (IRI). To overcome this, we apply an amplify-and-cancel method proposed in our previous work by Chen et al. into the successive amplify-and-forward (AF) relay model, thereby conceiving successive AF relaying with network interference cancellation. The proposed protocol benefits from low complexity because IRI is mitigated by linear analog processing without decoding any inter-relay signals. More specifically, a relay keeps receiving signals from the source and other relays before its own transmission, in order to obtain the prior knowledge of IRI that can be used to mitigate the IRI. We present an iterative expression of the power of the residual interference, based on which the achievable rate and the outage probability of the proposed scheme are derived. We further minimize its outage probability by optimizing the order of relays and present the optimal diversity and multiplexing tradeoff.
An Efficient MI Waveguide Based Underground Wireless Communication for Smart Irrigation

Underground (UG) wireless communication is challenging as the medium is heterogeneous composition of soil, rock, water, organic matter etc. The UG channel is subjected to change according to the location and weather conditions. Unlike the electromagnetic (EM) wave technique, Magnetic Induction (MI) technique can propagate in the dynamic UG channel with less path loss. In this paper, analyses on the performance of the underground channel using EM, MI and Magnetic Induction waveguide (MIWG) is carried. Path loss analysis of the channel is carried using the three techniques. The analysis is carried by varying the coil parameters like number of turns, radius of coil, angle of coil, wire resistance, relay interval etc. the optimization is also carried. The simulation results show that path loss in UG channel is reduced by 46% when MI waveguide technique is used instead of EM waves.
Changes in Tear Meniscus Height Following Lower Blepharoplasty as Measured by Optical Coherence Tomography

Purpose The lower eyelid serves important roles in tear distribution and drainage. The purpose of this study was to measure the tear meniscus height (TMH) with anterior segment optical coherence tomography after lower blepharoplasty. Methods A total of 52 eyes from 26 patients treated between July 2012 and June 2015 were included in the study. A transcutaneous or transconjunctival approach was performed, depending on whether (1) the supportive lower lid orbicularis oculi muscle was tightened, (2) the middle lamella was elongated, (3) minimal fat was removed or transpositioned, and (4) lateral canthal support was established. Marginal reflex distance 2 and marginal nose distance were analyzed with Image J software. TMH was measured with anterior segment optical coherence tomography. A paired t-test and Wilcoxon signed-rank test were used for statistical comparisons. Results Marginal reflex distance 2 decreased and marginal nose distance increased with both surgical techniques. TMH decreased from 337.3 ± 117.9 to 289.3 ± 69.1 µm (p = 0.024) in patients who had transcutaneous lower blepharoplasty, but increased from 186.5 ± 35.5 to 274.8 ± 58.3 µm (p = 0.000) in patients who had transconjunctival lower blepharoplasty. Medial and lateral TMHs decreased significantly from 228.8 ± 80.7 to 152.7 ± 42.1 µm (p = 0.008) in patients with transcutaneous lower blepharoplasty. TMH was significantly restored after lower blepharoplasty with either approach. Conclusions Enhancing the lower eyelid position combined with orbicularis muscle tightening and lateral canthal support can normalize the TMH following lower eyelid blepharoplasty.
Towards a Fast Off-Line Static Malware Analysis Framework

The profitability in cybercrime activity has resulted into an exponential growth of malware numbers and complexity. This has led to both industry and academic research building malware research labs to allow for deeper malware analysis so that for more efficient detection techniques can be proposed. Extended malware study could lead to development of more advanced malware signatures, potentially resulting into designing of secure systems thus a resilient cyberspace. Malware classification and clustering based on malware families and traits is an important step in malware analysis. This paper presents a comparative study of file format hashes that are used in the industry is conducted in an effort towards suggesting an approach for faster and easier offline malware classification framework.