Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining biomedical information from scientific literature

The rapid evolution and proliferation of a world-wide computerized network, the Internet, resulted in an overwhelming and constantly growing amount of publicly available data and information, a fact that was also verified in biomedicine. However, the lack of structure of textual data inhibits its direct processing by computational solutions. Information extraction is the task of text mining that intends to automatically collect information from unstructured text data sources. The goal of the work described in this thesis was to build innovative solutions for biomedical information extraction from scientific literature, through the development of simple software artifacts for developers and biocurators, delivering more accurate, usable and faster results. We started by tackling named entity recognition a crucial initial task with the development of Gimli, a machine-learning-based solution that follows an incremental approach to optimize extracted linguistic characteristics for each concept type. Afterwards, Totum was built to harmonize concept names provided by heterogeneous systems, delivering a robust solution with improved performance results. Such approach takes advantage of heterogenous corpora to deliver cross-corpus harmonization that is not constrained to specific characteristics. Since previous solutions do not provide links to knowledge bases, Neji was built to streamline the development of complex and custom solutions for biomedical concept name recognition and normalization. This was achieved through a modular and flexible framework focused on speed and performance, integrating a large amount of processing modules optimized for the biomedical domain. To offer on-demand heterogenous biomedical concept identification, we developed BeCAS, a web application, service and widget. We also tackled relation mining by developing TrigNER, a machine-learning-based solution for biomedical event trigger recognition, which applies an automatic algorithm to obtain the best linguistic features and model parameters for each event type. Finally, in order to assist biocurators, Egas was developed to support rapid, interactive and real-time collaborative curation of biomedical documents, through manual and automatic in-line annotation of concepts and relations. Overall, the research work presented in this thesis contributed to a more accurate update of current biomedical knowledge bases, towards improved hypothesis generation and knowledge discovery.
In Vitro Volume Imaging of Articular Cartilage Using Chirp-Coded High Frequency Ultrasound

Coded waveforms are frequently used as excitation pulses in ultrasonic systems e.g. to reduce noise and increase the penetration depth. The current work focuses on ultrasonic imaging of thick articular cartilage samples using a customized ultrasonic microscope scanning system. Long chirp-coded waveforms were employed on this system and investigated on the cartilage samples using both a matched-and Wiener-filter for wave compression. A short excitation pulse typically used in most commercial acoustic scanning microscope systems was also tested, and compared to the coded/compressed ones in terms of C-and B-scan images.
Effort-Reward Imbalance: A Risk Factor for Exposure to Workplace Bullying

Previous research shows that work environment factors are important antecedents of workplace bullying (WB), because of the stress they may induce. While previous studies have typically used Karasek’s Job Demand-Control model or the Demands-Resources model, the present study investigates whether another important occupational stress model, that is the Effort-Reward Imbalance model, is also associated to WB. A survey study in 19 Belgian organizations (n = 5727) confirmed that employees experiencing an imbalance between efforts and reward were more likely to be targets of exposure to bullying. In line with previous research, this study illustrates that stressful situations increase the risk of exposure to WB. It shows that the perceived incongruence between effort and reward may increase employee vulnerability to bullying. The perceived injustice may lead employees to engage in norm-breaking behavior and also signal low social standing to others, thereby potentially eliciting negative behaviors from others.
Dual-Input Grid-Connected Photovoltaic Inverter With Two Integrated DC–DC Converters and Reduced Conversion Stages

A dual-input dual-buck inverter (DI-DBI) with integrated boost converters (IBCs) is proposed for grid-connected applications. The proposed DI-DBI is composed of two buck-type inverter-legs and two IBCs. Two renewable dc sources, with independent maximum power point tracking, can be realized with the DI-DBI. The IBCs are used to provide a constant and stable dc-bus voltage for the inverter-legs, enabling the inverter to operate well, even when the dc source voltage varies widely and is lower than the peak amplitude of the grid voltage. Unlike a conventional two-stage dc–ac inverter, only a small ratio of the input power needs to be processed by the IBCs. The other part of the input power is directly fed to the grid through the inverter-legs; therefore, in comparison with a conventional dc–dc converter and inverter cascaded two-stage solution, the conversion stages of the DI-DBI, power rating, and losses of the IBCs are all reduced. The switching loss can be reduced as well because of the multivoltage-level characteristics of the proposed DI-DBI. As a result, a higher efficiency is achieved. Modulation and control strategies are proposed to realize independent regulation of the two dc inputs. Operation principles and characteristics of the proposed DI-DBI are analyzed in detail. Experimental results have been provided to verify the effectiveness and advantages of the proposed method.
Analysis and Evaluation of Pattern Division Multiple Access Scheme Jointed with 5G Waveforms

Nonorthogonal multiple access (NOMA) techniques represent a key feature for 5G systems in order to increase multiple users’ systems’ capacity. In particular, we propose, for study, a pattern division multiple access (PDMA) technique, which defines a pattern matrix used for mapping the users to a group of resource elements that might be shared by multiple users. The contribution of this paper is the analysis of the performances, in terms of bit error rate (BER), of 5G candidate waveforms, such as orthogonal frequency division multiplexing (OFDM), filter bank multi-carrier (FBMC), and generalized frequency division multiplexing (GFDM), in the PDMA scheme. Regarding the detection of different users’ data, the successive interference cancellation algorithm is performed at the receiver side. The simulation results, consolidated by the analytic study, exhibit that OFDM and FBMC could be used in the NOMA context, while the BER related to GFDM is very high.
IL-10 Producing B Cells Dampen Protective T Cell Response and Allow Chlamydia muridarum Infection of the Male Genital Tract

A significant proportion of individuals develop chronic, persistent and recurrent genital tract infections with Chlamydia trachomatis, which has been attributed to the numerous strategies that the bacterium uses to subvert host immune responses. Animal chlamydia models have demonstrated that protective immune response is mediated by CD4+ Th1 cytokine responses. Herein, we demonstrate that early after infecting the male genital tract, C. muridarum triggers the production of IL-10 by splenic and lymph node cells. In addition, C. muridarum triggers IL-6 and TNFα secretion. Data obtained from in vitro and in vivo experiments revealed B cells as the major IL-10 contributors. Indeed, purified B cells produced high amounts of IL-10 and also exhibited enhanced expression of inhibitory molecules such as CD39, PD-L1 and PD1 after C. muridarum stimulation. In vitro experiments performed with sorted cell subsets revealed that Marginal Zone B cells were the main IL-10 producers. In vitro and in vivo studies using TLR-deficient mice indicated that TLR4 signaling pathway was essential for IL-10 production. In addition, in vivo treatments to neutralize IL-10 or deplete B cells indicated that IL-10 and B cells played a significant role in delaying bacterial clearance ability. Moreover, the latter was confirmed by adoptive cell transfer experiments in which the absence of IL-10-producing B cells conferred the host a greater capability to induce Th1 responses and clear the infection. Interestingly, NOD mice, which were the least efficient in clearing the infection, presented much more Marginal Zone B counts and also enhanced TLR4 expression on Marginal Zone B cells when compared to B6 and BALB/c mice. Besides, treatment with antibodies that selectively deplete Marginal Zone B cells rendered mice more capable of inducing enhanced IFNγ responses and clearing the infection. Our findings suggest that B cells play a detrimental role in C. muridarum infection and that activation by innate receptors like TLR4 and IL-10 production by these cells could be used by Chlamydia spp. as a strategy to modulate the immune response establishing chronic infections in susceptible hosts.
A Novel Authenticated Key Agreement Protocol With Dynamic Credential for WSNs

Public key cryptographic primitive (e.g., the famous Diffie-Hellman key agreement, or public key encryption) has recently been used as a standard building block in authenticated key agreement (AKA) constructions for wireless sensor networks (WSNs) to provide perfect forward secrecy (PFS), where the expensive cryptographic operation (i.e., exponentiation calculation) is involved. However, realizing such complex computation on resource-constrained wireless sensors is inefficient and even impossible on some devices. In this work, we introduce a new AKA scheme with PFS for WSNs without using any public key cryptographic primitive. To achieve PFS, we rely on a new dynamic one-time authentication credential that is regularly updated in each session. In particular, each value of the authentication credential is wisely associated with at most one session key that enables us to fulfill the security goal of PFS. Furthermore, the proposed scheme enables the principals to identify whether they have been impersonated previously. We highlight that our scheme can be very efficiently implemented on sensors since only hash function and XOR operation are required.
Analysis, Design, and Implementation of a Single-Stage Multipulse Flexible-Topology Thyristor Rectifier for Battery Charging in Electric Vehicles

With the widespread growth in the use of electric vehicles (EVs), there is an urgent demand for fast-charging battery charger so as to alleviate the range anxiety for car users. A novel multipulse flexible-topology thyristor-based rectifier (mPFTTR) is proposed in this paper, which can be a suitable candidate for high-power fast-charging battery charger of the EVs. The proposed architecture has five operating modes: Series mode with 24 pulses (SM24P), parallel mode with 24 pulse, hybrid modes with 24 pulses (HM24P), series mode with 12 pulses, and parallel mode with 12 pulses. To meet the practical requirement of fast charging, the predictive current control strategy combined with multistep constant current charging (MSCCC) is also proposed, and followed by the detailed implementation and analysis. Simulation and experimental verifications are carried out, and both results show that the predictive current control strategy combined with the MSCCC method can charge the EVs with wide-range input voltage, and fast charging is also achieved. Finally, efficiency evaluation and comparisons of total harmonic distortions of the input current are developed between the proposed mPFTTR which operates in HM24P and the conventional thyristor-based rectifier that operates in SM24P.
Electrical Tree Characteristics in Polypropylene Under Impulse Superimposed DC Voltage in LN2

During the operation of the high-temperature superconducting (HTS) dc cable, due to the operation of the power electronic device, the cable insulation is subjected to the repeated impulse overvoltage while being subjected to the rated dc voltage, which easily generates the electrical tree and causes insulation failure. Polypropylene, the primary substrate for polypropylene laminated paper, faces a working environment in liquid nitrogen (LN2). The insulation performance of polypropylene in a low-temperature environment may change and affect its degradation process, threatening the safe and stable operation of power system. In this paper, a needle–plane electrode system was used to study the electrical tree characteristics in LN2. The results show that the tree morphology changes with different impulse frequency under dc-impulse voltage. With the increase in the positive dc voltage, the treeing process is accelerated. However, with the increase in the negative dc amplitude, the tree length decreases. With the impulse frequency increasing, the tree length increases significantly.
Evaluating Checkpoint Interval for Fault-Tolerance in MapReduce

MapReduce is the efficient framework for parallel processing of distributed big data in cluster environment. In such a cluster, task failures can impact on performance of applications. Although MapReduce automatically reschedules the failed tasks, it takes long completion time because it starts from scratch. The checkpointing mechanism is the valuable technique to avoid re-execution of finished tasks in MapReduce. However, defining incorrect checkpoint interval can still decrease the performance of MapReduce applications and job completion time. So, in this paper, checkpoint interval is proposed to avoid re-execution of whole tasks in case of task failures and save job completion time. The proposed checkpoint interval is based on five parameters: expected job completion time without checkpointing, checkpoint overhead time, rework time, down time and restart time. The experiments show that the proposed checkpoint interval takes the advantage of less checkpoints overhead and reduce completion time at failure time.
Hybrid Trefftz Finite-Element Method for Analyzing the Eigenmodes of Optical Fibers

We present a hybrid Trefftz finite-element method (HTFEM) for analyzing the eigenmodes of optical fibers. Using this method, waveguide problems that include open regions can be analyzed without requiring absorbing boundary conditions or utilizing artificial absorbers to enclose the region under analysis because the open region is treated as a special “Trefftz element. ” In addition, using a Trefftz element for the homogeneous core region and utilizing the Sakurai–Sugiura method to solve the nonlinear eigenvalue problems resulting from the HTFEM, we are able to obtain the degenerate effective indices of waveguides accurately without imposing symmetry conditions. We examine the convergence behavior of the effective indices of optical fibers computed using the HTFEM and compare the results with those obtained from analytic solutions or other numerical methods. Numerical examples demonstrate the validity of the formulation and the usefulness of eigenmode analyses using the HTFEM.
Evaluated Distribution Alignment for Heterogeneous Image Recognition

Heterogeneous domain adaptation has achieved promising results in solving the problem that source domain and target domain are sampled from not only different distributions but also different types of features. However, existing methods often only conduct distributed matching or feature alignment due to the challenges of HDA. Additionally, most methods often treated the marginal distribution adaptation and conditional distribution adaptation equally, which would result in poor performance in real applications. In this paper, we propose a Evaluated Distribution Alignment (EDA) approach to address these challenges. Our method is powered by two tactics: feature alignment and distribution alignment. It adaptively leverages the importance of the marginal and conditional distribution discrepancies and several transfer learning algorithms can be regarded as special cases of it. To evaluate the proposed method, we conduct extensive experiments on image classification, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods.
Identification of COP9 Signalosome Subunit Genes in Bactrocera dorsalis and Functional Analysis of csn3 in Female Fecundity

The COP9 signalosome (CSN) is an evolutionarily conserved multi-subunit complex that plays crucial roles in regulating various biological processes in plants, mammals, and the model insect Drosophila. However, it is poorly studied in non-model insects, whereas its role in fecundity remains unclear. In this study, all nine CSN subunits were identified and characterized in Bactrocera dorsalis, a major invasive agricultural tephritid pest. Each subunit gene, except for csn9x1, encoded a protein containing a PCI/PINT or MPN domain. Phylogenetic analysis revealed that all CSN subunits were individually clustered into a specific branch with their counterparts from other species. All CSN subunit genes were expressed in all detected developmental stages and tissues. Most subunits, except for csn8 and csn9x1, showed the highest expression level in the eggs. Notably, csn3 and csn5 were significantly enriched in mature female adults. Further analysis of csn3 revealed that it was enriched in the ovary and that its ovarian expression level gradually increased with the reproductive development process. RNAi-based knockdown of csn3 in female adults significantly reduced the number of laid eggs. The expression level of EcRB1 and USP, which encode the heterodimer receptors of 20E, and vitellogenin transcripts (Vg1 and Vg2) was suppressed in the fat body of female adults injected with csn3dsRNA. Decreased level of Vg1 protein was confirmed by means of Western blots. These data indicate that csn3 is involved in female reproduction by regulating 20E signaling and Vg synthesis. Overall, our study may facilitate the development of new strategies for controlling B. dorsalis since it provides insights into the evolution and expression patterns of all CSN subunit genes as well as the critical roles of csn3 in female fecundity.
Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks

Fault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks. The proposed scheme aims to provide fast fault type, phase, and location information for microgrid protection and service recovery. In the scheme, branch current measurements sampled by protective relays are pre-processed by discrete wavelet transform to extract statistical features. Then all available data is input into deep neural networks to develop fault information. Compared with previous work, the proposed scheme can provide significantly better fault type classification accuracy. Moreover, the scheme can also detect the locations of faults, which are unavailable in previous work. To evaluate the performance of the proposed fault detection scheme, we conduct a comprehensive evaluation study on the CERTS microgrid and IEEE 34-bus system. The simulation results demonstrate the efficacy of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.
Herpes Simplex Virus Type 2 Immediate Early Protein ICP27 Inhibits IFN-β Production in Mucosal Epithelial Cells by Antagonizing IRF3 Activation

Herpes simplex virus type 2 (HSV-2) is the main cause of genital herpes and infections are common in the lower genital tract. Although neuronal and immune cells can be infected, epithelial cells, and keratinocytes are the primary HSV-2 target cells. HSV-2 establishes latency by evading the host immune system and its infection can also increase the risk of HIV-1 sexual transmission. Our pervious study found that HSV-2 immediate early protein ICP22, inhibited IFN-β production by interfering with the IRF3 pathway. However, ICP22-null HSV-2 did not completely lose the capability of suppressing IFN-β induction, suggesting the involvement of other viral components in the process. In this study, by using an ex vivo cervical explant model, we first demonstrated that HSV-2 can indeed inhibit IFN-β induction in human mucosal tissues. We further identified HSV-2 immediate early protein ICP27 as a potent IFN-β antagonist. ICP27 significantly suppresses the Sendai virus or polyinosinic-polycytidylic acid-induced IFN-β production in human mucosal epithelial cells, showing that ICP27 inhibits the IFN-β promoter activation, and IFN-β production at both mRNA and protein levels. Additional studies revealed that ICP27 directly associates with IRF3 and inhibits its phosphorylation and nuclear translocation, resulting in the inhibition of IFN-β induction. Our findings provide insights into the molecular mechanism underlying HSV-2 mucosal immune evasion, and information for the design of HSV-2 mucosal vaccines.
Generation-Storage Coordination for Islanded DC Microgrids Dominated by PV Generators

For the islanded dc microgrids, the photovoltaic (PV) generators and storages are the main components to provide power and maintain the system stability. To avoid the dc bus voltage rise, the overcharge and overdischarge of storages, the coordination between generations and storages is critically important. In this paper, a decentralized control method is proposed to realize the generation-storage coordination based on the dc bus voltage signaling. First, a novel state-of-charge (SOC) based droop control strategy is designed for storages to provide dc bus voltage support and balance their SOCs, which can prevent the overuse of a certain storage. Second, an adaptive power control strategy is applied to PV generators, which can seamlessly regulate the output power of PV generators off maximum power point tracking mode according to the dc bus voltage. Combining these two strategies through the dc bus voltage signaling, the generation-storage coordination can be realized. Furthermore, the stability of the proposed control method is also analyzed. All the conclusions are verified by the real-time hardware-in-loop tests.
Multitask Pointer Network for Korean Dependency Parsing

Dependency parsing is a fundamental problem in natural language processing. We introduce a novel dependency-parsing framework called head-pointing--based dependency parsing. In this framework, we cast the Korean dependency parsing problem as a statistical head-pointing and arc-labeling problem. To address this problem, a novel neural network called the multitask pointer network is devised for a neural sequential head-pointing and type-labeling architecture. Our approach does not require any handcrafted features or language-specific rules to parse dependency. Furthermore, it achieves state-of-the-art performance for Korean dependency parsing.