Miracle-dz/UniFAR-FTUs huggingface dataset:
HLA-G: Too Much or Too Little? Role in Cancer and Autoimmune Disease

HLA-G is a non-classical HLA class I molecule with immunomodulatory properties. It was initially described at the maternal-fetal interface, and it was later found that this molecule was constitutively expressed on certain immuneprivileged tissues, such as cornea, endothelial and erythroid precursors, and thymus. The immunosuppressive effect of HLA-G is exerted through the interaction with its cognate receptors, expressed on immunocompetent cells, like ILT2, expressed on NK, B, T cells and APCs; ILT4, on APCs; KIR, found on the surface of NK cells; and finally, the co-receptor CD8. Because of these immunomodulatory functions, HLA-G has been involved in several processes, amongst which organ transplantation, viral infections, cancer progression, and autoimmunity. HLA-G neo-expression on tumors has been recently described in several types of malignancies. In fact, tumor progression is tightly linked to the presence of the molecule, as it exerts its tolerogenic function, inhibiting the cells of the immune system and favoring tumor escape. Several polymorphisms in the 3’UTR region condition changes in HLA-G expression (14bp and +3142C/G, among others), which have been associated with both the development and outcome of patients with different tumor types. Also, in recent years, several studies have shown that HLA-G plays an important role in the control of autoimmune diseases. The ability of HLA-G to limit the progression of these diseases has been confirmed and, in fact, levels of the molecule and several of its polymorphisms have been associated with increased susceptibility to the development of autoimmune diseases, as well as increased disease severity. Thus, modulating HLA-G expression in target tissues of oncology patients or patients with autoimmune diseases may be potential therapeutic approaches to treat these pathological conditions.
How Environmental Leadership Boosts Employees’ Green Innovation Behavior? A Moderated Mediation Model

We examined employees’ green organizational identity as a mediator and green organizational climate as a moderator in the relationship between environmental leadership and follower green innovation behavior. Through collecting data (N = 313) from public organizations in China at different times, we found that environmental leadership is positively related to employees’ green innovation behavior through increasing their green organizational identity. Meanwhile, the mediating relationship is conditional on the moderator of green organizational climate. The current study aims to clarify the mechanism and boundary condition in the relationship between environmental leadership and employees’ green innovation behaviors.
Bi-Stable Perception: Self-Coordinating Brain Regions to Make-Up the Mind

Bi-stable perception is a strong instance of cognitive self-organization, providing a research model for how ‘the brain makes up its mind. ’ The complexity of perceptual bistability prevents a simple attribution of functions to areas, because many cognitive processes, recruiting multiple brain regions, are simultaneously involved. The functional magnetic resonance imaging (fMRI) evidence suggests the activation of a large network of distant brain areas. Concurrently, electroencephalographic and magnetoencephalographic (MEEG) literature shows sub second oscillatory activity and phase synchrony on several frequency bands. Strongly represented are beta and gamma bands, often associated with neural/cognitive integration processes. The spatial extension and short duration of brain activities suggests the need for a fast, large-scale neural coordination mechanism. To address the range of temporo-spatial scales involved, we systematize the current knowledge from mathematical models, cognitive sciences and neuroscience at large, from single-cell- to system-level research, including evidence from human and non-human primates. Surprisingly, despite evidence spanning through different organization levels, models, and experimental approaches, the scarcity of integrative studies is evident. In a final section of the review we dwell on the reasons behind such scarcity and on the need of integration in order to achieve a real understanding of the complexities underlying bi-stable perception processes.
The Role of Wideband Tympanometry in the Diagnosis of Meniere's Disease

Meniere's disease (MD) is a clinical syndrome characterized by spontaneous recurrent vertigo, usually accompanied by hearing loss, tinnitus, and aural fullness. The cause of MD remains unclear and is generally considered to be associated with endolymphatic hydrops. Studies showed that patients with MD could have eustachian tube dysfunction (ETD). ETD can disrupt the pressure balance between the middle and inner ear and impair the inner ear's function. In recent years, several studies have attempted to identify MD by using wideband tympanometry (WBT). However, there are limited studies in this area. There is no consensus on how to use WBT to diagnose Meniere's disease. Therefore, we endeavored to conduct a narrative review in this aspect based on the latest research findings. Reduction in resonance frequency and absorbance are characteristic of MD and can identify Meniere's disease. The use of an increase in the integrated area of absorbance as an indicator for identifying MD is controversial. WBT seems to be ineffective as a diagnostic tool during the acute episodes of Meniere's disease. Patients with MD may benefit from WBT. WBT has excellent potential for future use in Meniere's disease. However, further large sample sizes, multicenter studies are needed.
A Non-Linear Association of Triglyceride Glycemic Index With Cardiovascular and All-Cause Mortality Among Patients With Hypertension

Background To investigate the association between insulin resistance (IR), quantified by triglyceride glycemic index (TyG index), cardiovascular mortality (CVM), and all-cause mortality (ACM) in hypertension patients. Methods We included 8,554 patients with hypertension aged ≥18 years old from the 1999–2014 National Health and Nutrition Examination Surveys (NHANES). The status of CVM and ACM of participants were followed through December 31, 2015. Cox proportional hazards models and Kaplan-Meier survival curves were used to evaluate the relationship between TyG index, CVM, and ACM. Results During a median of 82 months follow-up, 1,882 mortality cases had occurred, 434 of which were due to cardiovascular disease. The patients with hypertension with TyG ≥ 10 were older, had a higher chance of being smokers, were obese, had higher blood pressure, and had risk or had cardiovascular disease. In Cox proportional hazards models, compared with the patients with TyG <8, those with TyG ≥ 10 had 56% increased risk for ACM. On the other hand, no significant difference for CVM between the four groups were observed. In the restricted cubic spline regression models, the relationship between TyG index and ACM was non-linear. Subgroup analysis showed non-linear relationship between TyG index and ACM in elderly patients aged ≥60 years. The cut-off value of TyG for ACM was 9.45, and those with higher or lower than 9.45 had more risk of ACM. When TyG index was more than 9.52, the risk for CVM would increase among the whole group. Kaplan-Meier survival curves showed patients with TyG ≥ 10 had higher risk of ACM and CVM (Log rank P < 0.05). Conclusions We demonstrated that the association between ACM and TyG index in elderly patients with hypertension aged ≥60 years was non-linear. However, TyG index was only more than 9.52, hence, the risk for CVM would increase among the whole hypertension group.
Dominant Antiviral CD8+ T Cell Responses Empower Prophylactic Antibody-Eliciting Vaccines Against Cytomegalovirus

Human cytomegalovirus (HCMV) is an ubiquitous herpesvirus that can cause serious morbidity and mortality in immunocompromised or immune-immature individuals. A vaccine that induces immunity to CMV in these target populations is therefore highly needed. Previous attempts to generate efficacious CMV vaccines primarily focused on the induction of humoral immunity by eliciting neutralizing antibodies. Current insights encourage that a protective immune response to HCMV might benefit from the induction of virus-specific T cells. Whether addition of antiviral T cell responses enhances the protection by antibody-eliciting vaccines is however unclear. Here, we assessed this query in mouse CMV (MCMV) infection models by developing synthetic vaccines with humoral immunity potential, and deliberately adding antiviral CD8+ T cells. To induce antibodies against MCMV, we developed a DNA vaccine encoding either full-length, membrane bound glycoprotein B (gB) or a secreted variant lacking the transmembrane and intracellular domain (secreted (s)gB). Intradermal immunization with an increasing dose schedule of sgB and booster immunization provided robust viral-specific IgG responses and viral control. Combined vaccination of the sgB DNA vaccine with synthetic long peptides (SLP)-vaccines encoding MHC class I-restricted CMV epitopes, which elicit exclusively CD8+ T cell responses, significantly enhanced antiviral immunity. Thus, the combination of antibody and CD8+ T cell-eliciting vaccines provides a collaborative improvement of humoral and cellular immunity enabling enhanced protection against CMV.
Research on Amazon Forest Fire Based on Cellular Automata Simulation

Wildfire, especially uncontrolled flammable vegetation hazard in rural or wilderness areas, seriously affected human life and safety, prediction methods has been researched widely in recent decades. According to Amazon forest fire situation, an advanced system hierarchical clustering model is established in this paper, and the diffusion simulations of Amazon forest fire is implemented by using the cellular automata model, which constructed with the previous model. Then, the diffusion speed and diffusion range of different brightness parameters are analyzed. Furthermore, combined with the three-dimensional effect of fire and smoke simulated by particle system, the entropy weight TOPSIS model is constructed to the optimized ranking of wildfire risk index with its longitude and latitude, and obtain the relationship diagram of latitude, longitude and wildfire risk index after extracting data. The feasibility and rationality of the proposed method is verified by simulation results.
A Community-Based Method for Identifying Influential Nodes using Network Embedding

People's influence on their friends' personal opinions and decisions is an essential feature of social networks, which has led to many businesses using social media to convince a small number of users to increase awareness and ultimately maximize sales to the maximum number of users. This issue is typically expressed as the influence maximization problem. In this paper, we will identify the most influential nodes in the social network during two phases. In the first phase, we offer a community detection approach based on the Node2Vec method to detect the potential communities. In the second phase, larger communities are chosen as candidate communities, and then the heuristic-based measurement approach is utilized to identify influential nodes within candidate communities. Evaluations of the proposed method on two real datasets show the superiority of this method over other compared methods.
Model-Free Predictive Control based on ARX Representation: A Comparative Assessment with Proportional-Integral Regulator

This paper presents a performance comparison between the well-known proportional-integral regulator based on pulse-width-modulation (PWM) and the novel model-free predictive control for the current regulation of a three-phase two-level voltage-source inverter. Recently, model-free predictive control has been proposed to provide an accurate prediction of the current control without previous knowledge of the physical system. This approach is realized with a recursive least squares algorithm to identify the parameters of an auto-regressive with exogenous input model. Simulated results verify the high performance of model-free predictive control compared to the linear regulator under model parameter mismatch.
Boosting the Intelligibility of Waveform Speech Enhancement Networks through Self-supervised Representations

The ultimate goal of speech enhancement is to improve speech quality and intelligibility. Integrating human speech elements into waveform denoising neural networks has proven to be a simple yet effective strategy for this purpose. Such integration, however, has mostly been carried out within supervised learning settings, without taking advantage of the power of the latest self-supervised learning models, which have demonstrated remarkable capability of extracting knowledge from large training sets. In this paper, we present K-SENet, a knowledge-assisted waveform framework for speech enhancement. Wave-U-Net is utilized as the baseline model and the foundation to build our framework. To achieve enhanced intelligibility, we propose a perceptual loss function that relies on self-supervised speech representations pretrained on large datasets, to provide guidance for the baseline network. Wav2vec and PASE are the choices of self-supervised models in this work. Our proposed perceptual loss is calculated upon the perceptual similarities captured by the speech representations. Minimizing this loss would ensure the denoised network outputs sound like clean human speeches. Experiments on the Noisy VCTK and modified TIMIT datasets demonstrate that our K-SENet can significantly improve the perceptual quality of network outputs.
Sequential Adoption of Package Technologies: The Dynamics of Stacked Trait Corn Adoption

Genetically modified corn seed companies have innovated continuously with the introduction of new traits and, more recently, with the creation of stacked varieties. This work develops a Bayesian model of adoption that demonstrates how uncertainty with a package technology can lead to a sequential adoption pattern in which farmers move from single trait to stacked varieties. We then develop a semi-parametric panel data model of adoption to measure the effects of experience on the adoption of stacked varieties. The results underscore the importance of early experience with the non-stacked technology in the subsequent adoption of stacked varieties. There is also evidence that farmers with more human capital tend to learn faster from own experience and that as time evolves, the importance of early experience decreases.
Reverse Engineering Pedagogy as an Educational Tool to Promote Symbiosis between Design and Physics

In this work, we introduce our reverse engineering pedagogy (REP), which aims to serve as a means for symbiosis of design thinking and physics. The objective of the approach is to create an enabler for engineering students to approach their design problems through the lens provided by first-principle physics. We discuss our DA3D framework for the organization of such activities and describe how they may be incorporated in the classrooms. We also chronicle our implementation of the reverse engineering activities in our introductory undergraduate year 1 electromagnetism course, where two activities involving the dissection of aluminum electrolytic capacitor and induction cooker are introduced. The overall results show an increase in self-efficacy of the students on use of physics concepts in their design processes.
Experimental Investigation of the Wind Pressure Distributions on a Typical Tall Residential Building

A detailed test of the wind pressure distribution of a tall residential building was carried out in a Boundary Layer Wind Tunnel.The wind pressure distributions on the building were investigated.The results have shown that the correlations of wind pressures on the concave surface are very high at the same height.For the design of the double cantilevered roof,the upper is controlled by negative wind pressure and the lower by positive wind pressure.The glass tailgate at the top of the building is also controlled by positive wind pressure.The wind pressure distributions of the building are changed more noticeably if the surrounding buildings and topography exist.Especially under the height of the disruptors,the change of fluctuating wind pressure is significant.The min peak wind pressure has an increase of 28% when the building is disturbed.
Faraday Rotation Estimation Based on PolSAR Data: A Denoising Technique

Low-frequency spaceborne polarimetric radar system is susceptible to the ionospheric Faraday rotation (FR). High-quality FR estimates are required for advanced polarimetric and ionospheric applications. Noise reduction plays a key role in higher quality FR estimation. Based on the Baran filter and Bickel-Bates FR estimator, this paper proposes a denoising technique for FR estimation using spaceborne polarimetric SAR data. Experiments on L-band ALOSPALSAR data of Alaska demonstrate that this technique can achieve nice noise smoothing of FR estimates.
Robustifying DARTS by Eliminating Information Bypass Leakage via Explicit Sparse Regularization

Differentiable architecture search (DARTS) is a promising end to end NAS method which directly optimizes the architecture parameters through general gradient descent. However, DARTS is brittle to the catastrophic failure incurred by the skip connection in the search space. Recent studies also cast doubt on the basic underlying hypotheses of DARTS which are argued to be inherently prone to the performance discrepancy between the continuous-relaxed supernet in the training phase and the discretized finalnet in the evaluation phase. We Figure out that the robustness problem and the skepticism can both be explained by the information bypass leakage during the training of the supernet. This naturally highlights the vital role of the sparsity of architecture parameters in the training phase which has not been well developed in the past. We thus propose a novel sparse-regularized approximation and an efficient mixed-sparsity training scheme to robustify DARTS by eliminating the information bypass leakage. We subsequently conduct extensive experiments on multiple search spaces to demonstrate the effectiveness of our method.
Visualization-based Multi-Criterion Decision Making with NIMBUS Method Using Palette Viz

After many efficient evolutionary many-objective optimization (EMaO) algorithms have been proposed and demonstrated to find multiple well-distributed near Pareto-optimal solutions, one main emphasis now is to combine them with suitable multi-criterion decision-making (MCDM) approaches to choose a single preferred solution. For integrating MCDM approaches with EMaO algorithms, there have been growing interests in implementing MCDM concepts algorithmically within EMaOs, but in this paper, we propose a visualization-based MCDM-EMaO integration implementing the well-known NIM-BUS method using a recently proposed PaletteViz visualization technique and demonstrate its working by applying it to two test problems and an engineering design problem. The detailed results show the usefulness of the Palette Viz procedure in assisting decision-makers to implement MCDM approaches with a better understanding of trade-off solutions.
MO-MFCGA: Multiobjective Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

Multiobjetive optimization has gained a considerable momentum in the evolutionary computation scientific community. Methods coming from evolutionary computation have shown a remarkable performance for solving this kind of optimization problems thanks to their implicit parallelism and the simultaneous convergence towards the Pareto front. In any case, the resolution of multiobjective optimization problems (MOPs) from the perspective of multitasking optimization remains almost unexplored. Multitasking is an incipient research stream which explores how multiple optimization problems can be simultaneously addressed by performing a single search process. The main motivation behind this solving paradigm is to exploit the synergies between the different problems (or tasks) being optimized. Going deeper, we resort in this paper to the also recent paradigm Evolutionary Multitasking (EM). We introduce the adaptation of the recently proposed Multifactorial Cellular Genetic Algorithm (MFCGA) for solving MOPs, giving rise to the Multiobjective MFCGA (MO-MFCGA). An extensive performance analysis is conducted using the Multiobjective Multifactorial Evolutionary Algorithm as comparison baseline. The experimentation is conducted over 10 multitasking setups, using the Multiobjective Euclidean Traveling Salesman Problem as benchmarking problem. We also perform a deep analysis on the genetic transferability among the problem instances employed, using the synergies among tasks aroused along the MO-MFCGA search procedure.
Multi-Slice Clustering for 3-order Tensor

Several methods of triclustering of three dimensional data require the specification of the cluster size in each dimension. This introduces a certain degree of arbitrariness. To address this issue, we propose a new method, namely the multi-slice clustering (MSC) for a 3-order tensor data set. We analyse, in each dimension or tensor mode, the spectral decomposition of each tensor slice, i.e. a matrix. Thus, we define a similarity measure between matrix slices up to a threshold (precision) parameter, and from that, identify a cluster. The intersection of all partial clusters provides the desired triclustering. The effectiveness of our algorithm is shown on both synthetic and real-world data sets.
Design and Synchronization of Chaos-Based True Random Number Generators and Its FPGA Implementation

In this paper, a new chaos-based true random number generator (TRNG) and a sliding mode controller (SMC) to synchronize the proposed TRNGs are proposed. Firstly, the continuous chaotic system is transformed into a discrete system that preserves the original continuous system’s chaotic behavior and makes it easy to realize with the field-programmable gate array (FPGA) for synchronization control. Then, a discrete SMC is introduced to solve the synchronization problem of the master-slave discrete chaotic systems. Subsequently, a novel hybrid function integrated with the El-Gamal algorithm is proposed to complete the TRNG design. Finally, the FPGA-based realization of the synchronized master-slave TRNGs is implemented. The randomness quality of the proposed TRNGs has been ensured by using Shannon’s entropy and histogram analysis. Furthermore, the National Institute of Standards and Technology (NIST) test suite has also been introduced to evaluate the proposed TRNGs and compare with the existing results in the literature.