Miracle-dz/UniFAR-FTUs huggingface dataset:
Proteomic Analysis Dissects Molecular Mechanisms Underlying Plant Responses to Phosphorus Deficiency

Phosphorus (P) is an essential nutrient for plant growth. In recent decades, the application of phosphate (Pi) fertilizers has contributed to significant increases in crop yields all over the world. However, low efficiency of P utilization in crops leads to intensive application of Pi fertilizers, which consequently stimulates environmental pollution and exhaustion of P mineral resources. Therefore, in order to strengthen the sustainable development of agriculture, understandings of molecular mechanisms underlying P efficiency in plants are required to develop cultivars with high P utilization efficiency. Recently, a plant Pi-signaling network was established through forward and reverse genetic analysis, with the aid of the application of genomics, transcriptomics, proteomics, metabolomics, and ionomics. Among these, proteomics provides a powerful tool to investigate mechanisms underlying plant responses to Pi availability at the protein level. In this review, we summarize the recent progress of proteomic analysis in the identification of differential proteins that play roles in Pi acquisition, translocation, assimilation, and reutilization in plants. These findings could provide insights into molecular mechanisms underlying Pi acquisition and utilization efficiency, and offer new strategies in genetically engineering cultivars with high P utilization efficiency.
Electrospun Chitosan Functionalized with C12, C14 or C16 Tails for Blood-Contacting Medical Devices

Medical applications stimulate the need for materials with broad potential. Chitosan, the partially deacetylated derivative of chitin, offers many interesting characteristics, such as biocompatibility and chemical derivatization possibility. In the present study, porous scaffolds composed of electrospun interwoven nanometric fibers are produced using chitosan or chitosan functionalized with aliphatic chains of twelve, fourteen or sixteen methylene groups. The scaffolds were thoroughly characterized by SEM and XPS. The length of the aliphatic tail influenced the physico-chemical and dynamic mechanical properties of the functionalized chitosan. The electrospun membranes revealed no interaction of Gram+ or Gram− bacteria, resulting in neither antibacterial nor bactericidal, but constitutively sterile. The electrospun scaffolds demonstrated the absence of cytotoxicity, inflammation response, and eryptosis. These results open the door to their application for blood purification devices, hemodialysis membranes, and vascular grafts.
Numerical and Experimental Aerodynamic Investigations of Boundary Layer Ingestion for Improving Propulsion Efficiency of Future Air Transport

This paper presents the recent investigations of the Boundary Layer Ingestion (BLI) concept conducted at ONERA. Both numerical and experimental investigations of a BLI propulsion system are conducted synergistically with the objectives of preparing and validating both the numerical simulations tools and the experimental methodologies that will be necessary for the design of a future transport aircraft incorporating a BLI propulsive system. A test rig dedicated to the investigation of BLI has been specifically designed manufactured and tested in the ONERA L1 wind tunnel in Lille in low-speed conditions. This experimental rig embeds an electrically powered fan and several measurements capabilities including separate measurements of the forces acting on the different model components, thus allowing a in depth investigations and quantification of the mechanisms of BLI. Numerical simulations of this test rig, based on RANS CFD calculations using the Actuator Disk (AD) approach to model the effect of the fan absorbing a boundary layer flow have been used, first to help defining the experimental test rig and then, using the experimental results to establish and validate a numerical simulation approach which could be used in the future to facilitate the integration of a BLI propulsion system on a future transport aircraft.
Multi-function microwave components based on implantable filters

This paper presents some multi-function microwave components based on implantable filters. It is found that some bandpass filters (BPFs) have the properties of ±K-inverters, meaning that they can be utilised to replace 90° or 270° transmission lines in passive microwave components such as in-phase and out-of-phase power dividers and rat-race couplers to realise both filtering and power-splitting functions. The ±K-inverter properties also mean that the proposed filters can be employed as impedance transformers in output-matching networks (OMNs) of power amplifiers for efficiency improvement and harmonic suppression. As a demonstration, some power combining and dividing components and power amplifiers are implemented based on the implantable filters, which exhibit dual functions, good performance and compact size.
Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions

Advances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using machine learning as opposed to conventional FDTD simulations. The machine learning (ML) based methods can not only perform with high accuracy and return optical spectra and optimal design parameters, but also maintain a stable high computing efficiency without being affected by the structural complexity. This work reviews the prominent ML methods involved in forward simulation and inverse design of plasmonic nanomaterials, such as Convolutional Neural Networks, Generative Adversarial Networks, Genetic Algorithms and Encoder–Decoder Networks. Moreover, we acknowledge the current limitations of ML methods in the context of plasmonics and provide perspectives on future research directions.
Comprehensive Review Regarding Mercury Poisoning and Its Complex Involvement in Alzheimer’s Disease

Mercury (Hg) is considered one of the most widespread toxic environmental pollutants, which seems to have multiple effects on organisms even at low concentrations. It has a critical role in many health problems with harmful consequences, with Hg primarily targeting the brain and its components, such as the central nervous system (CNS). Hg exposure was associated with numerous CNS disorders that frequently trigger Alzheimer’s disease (AD). Patients with AD have higher concentrations of Hg in blood and brain tissue. This paper aims to emphasize a correlation between Hg and AD based on the known literature in the occupational field. The outcome shows that all these concerning elements could get attributed to Hg. However, recent studies did not investigate the molecular level of Hg exposure in AD. The present review highlights the interactions between Hg and AD in neuronal degenerations, apoptosis, autophagy, oxidative stress (OS), mitochondrial malfunctions, gastrointestinal (GI) microflora, infertility and altering gene expression.
3GAN: A Three-GAN-based Approach for Image Inpainting Applied to the Reconstruction of Occluded Parts of Building Walls

Realistic representation of building walls from images is an important aspect of scene understanding and has many applications. Often, images of buildings are the only input for texturing 3D models, and these images may be occluded by vegetation. One task of image inpainting is to remove these clutter objects. Since the disturbing objects can also be of a larger scale, modern deep learning techniques should be applied to replace them as realistically and context-aware as possible. To support an inpainting network, it is useful to include a-priori information. An example of a network that considers edge images is the two-stage GAN model denoted as EdgeConnect. This idea is taken up in this work and further developed to a three-stage GAN (3GAN) model for façade images by additionally incorporating semantic label images. By inpainting the label images, not only a clear geometric structure but also class information, like position and shape of windows and their typical color distribution, are provided to the model. This model is compared qualitatively and quantitatively with the conventional version of EdgeConnect and another well-known deep-learning-based approach on inpainting which is based on partial convolutions. This latter approach was outperformed by both GAN-based methods, both qualitatively and quantitatively. While the quantitative evaluation showed that the conventional EdgeConnect method performs minimally best, the proposed method yields a slightly better representation of specific façade elements.
Designing Animated Transitions for Dynamic Streaming Big Data

Visualizations for Streaming Big Data need to handle high volumes of information in real-time, making it challenging to convey significant data changes without confusing users. A simple first approach would be switching from the current visual idiom to another, highlighting a significant change. Unfortunately, there are no guidelines to design effective transitions between two visual idioms in Streaming Big Data. Therefore, we created a tree of animation concepts to serve as a starting point for designing such animated transitions. The concepts represent several ways in which a visual idiom can be transformed into another. We chose three visual idioms to test our idea and arranged several concepts to apply at each possible pairing (six possibilities). For each pairing, we tested the accuracy of people’s perceptions. Finally, we conducted a user study with 100 participants, where each participant answered various questions about transitions between two visual idioms shown in several videos. We concluded that to conceive appropriate animated transitions for Streaming Big Data (which also applies just for Data Streaming) that allow users to understand the changes in incoming data, varying how the proposed concepts are applied is not enough, highlighting the need for future research to address this challenge.
Topological Superconducting Transition Characterized by a Modified Real-Space-Pfaffian Method and Mobility Edges in a One-Dimensional Quasiperiodic Lattice

A modified real-space-Pfaffian method is applied to characterize the topological superconducting transition of a one-dimensional p-wave superconductor with quasiperiodic potentials. We found that the Majorana zero-energy mode exists in the topological non-trivial phase, and its spatial distribution is localized at ends of the system, whereas in the topological trivial phase, there is no Majorana zero mode. Furthermore, we numerically found that due to the competition between the localized quasi-disorder and the extended p-wave pairing, there are mobility edges in the energy spectra. Our theoretical work enriches the research on the quasiperiodic p-wave superconducting models.
Spatial User Interaction: What Next?

Spatial user interaction refers to interactions that use spatial attributes or spatial relations between entities to trigger system functions. Spatial user interaction can offer intuitive interactions between users and their environment, particularly in ubiquitous environments where interconnected objects are in constant interaction. Many research works demonstrated the relevance of the spatial user interaction paradigm. Nevertheless, only a few were conducted to standardize the necessary concepts for designing spatial applications and to propose software tools that support spatial user interaction. The present work reviews the literature for spatial user interaction design and development. It analyses papers dealing with applications supporting spatial user interaction. It shows a lack of generic tools for designing/developing applications supporting spatial user interactions and proposes directions for future research work in this field.
Trends in Renewable Electricity Generation in the G20 Countries: An Analysis of the 1990–2020 Period

The use of electricity from non-renewable sources increases environmental impacts. Therefore, several countries have committed to increase the use of renewable sources. Considering the importance of the Group of Twenty (G20), this article aims to identify trends in renewable electricity supply in these countries. The data collected are from the International Energy Agency (IEA) between 1990 and 2020. The methods measured the production of each type of electricity in the G20 matrices and identified the influence of the population, Gross Domestic Product (GDP), and CO2 emissions in renewable electricity production using multiple linear regression. In terms of results, Brazil and Canada have the most renewable electricity matrices and higher per capita renewable production than non-renewable. Saudi Arabia presented the smallest renewable matrix throughout the analyzed period. All 20 countries have varied electrical production, with different amounts of solar, wind, hydro, biomass, geothermal, and tidal energy. Countries with the highest GDP are not necessarily the largest producers of renewable electricity. Hydroelectric energy, the biggest highlight in renewable production, is making room for other sources such as wind and solar, which grew the most in terms of participation in the electrical matrices. The waste, geothermal, and tidal energy participation have shown a timid but constant growth.
How evolution builds up complexity?: In vitro evolution approaches to witness complexification in artificial molecular replication systems

How can evolution assemble lifeless molecules into a complex living organism? The emergent process of biological complexity in the origin of life is a big mystery in biology. In vitro evolution of artificial molecular replication systems offers unique experimental opportunities to probe possible pathways of a simple molecular system approaching a complex life-like system. This review focuses on experimental efforts to examine evolvability of molecules in vitro from the pioneering Spiegelman’s experiment to our latest research on an artificial RNA self-replication system. Genetic translation and compartmentalization are shown to enable sustainable replication and evolution. Latest studies are revealing that coevolution of self-replicating “host replicators” and freeloading “parasitic replicators” is crucial to extend evolvability of a molecular replication system for continuous evolution and emergence of diversity. Intense competition between hosts and parasites would have existed even before the origin of life and contributed to generating complex molecular ecosystems. This review article is an extended version of the Japanese article “An in vitro evolutionary journey of an artificial RNA replication system towards biological complexity” published in SEIBUTSU-BUTSURI Vol.61, p.240–244 (2021). ”
Evaluation of Jupiter: A Lightweight Code Review Framework

Software engineers generally agree that code reviews reduce development costs and improve software quality by finding defects in the early stages of software development. In addition, code review software tools help the code review process by providing a more efficient means of collecting and analyzing code review data. On the other hand, software organizations that conduct code reviews often do not utilize these review tools. Instead, most organizations simply use paper or text editors to support their code review processes. Using paper or a text editor is potentially less useful than using a review tool for collecting and analyzing code review data. In this research, I attempted to address the problems of previous code review tools by creating a lightweight and flexible review tool. This review tool that I have developed, called “Jupiter”, is an Eclipse IDE Plug-In. I believe the Jupiter Code Review Tool is more efficient at collecting and analyzing code review data than the text-based approaches. To investigate this hypothesis, I have constructed a methodology to compare the Jupiter Review Tool to the text-based review approaches. I carried out a case study using both approaches in a software engineering course with 19 students. The results provide some supporting evidence that Jupiter is more useful and more usable than the text-based code review, requires less overhead than the text-based review, and appears to support long-term adoption. The major contributions of this research are the Jupiter design philosophy, the Jupiter Code Review Tool, and the insights from the case study comparing the text-based review to the Jupiter-based review.
Multi-branch hidden Markov models for remaining useful life estimation of systems under multiple deterioration modes

Remaining Useful Life (RUL) estimation plays an important role in implementing a condition-based maintenance (CBM) program, since it could provide sufficient time for maintenance crew to act before an actual system failure. This prognostic task becomes harder when several deterioration mechanisms co-exist within the same system due to the variability and dynamics of its operating environment, since the RUL obviously depends on the mode that the system is following. In this paper, we propose a multi-branch modeling framework to deal with such problems. The proposed model consists of several branches in which each one represents a deterioration mode and is considered as a hidden Markov model. The system’s conditions are modeled by several discrete meaningful states, such as “good”, “minor defect”, “maintenance required” and “failure”, which would be easy to interpret for maintenance personnel. Furthermore, these states are considered to be “hidden” and can only be revealed through observations. These observations are the condition monitoring information in the CBM context. The performance of the proposed model is evaluated through numerical studies. The results show that the multi-branch model can outperform the standard one-branch HMM model in RUL estimation, especially when the “distance” between the deterioration modes is considerable.
A Critical Review on the Impacts of Energy Storage Systems and Demand-Side Management Strategies in the Economic Operation of Renewable-Based Distribution Network

Energy storage systems (ESSs) and demand-side management (DSM) strategies have significant potential in providing flexibility for renewable-based distribution networks. Therefore, combining ESSs and DSM strategies with renewable energy sources (RESs) to solve economic, operational, environmental, and power-related political issues has received special attention from power system planners around the world. In this regard, developed countries, which are pioneers in renewable technologies, have proposed various supportive policies and practices for the widespread use of ESSs and DSM strategies in the context of distribution networks. Hence, this paper performs a comprehensive review of the most recent actions taken to build the infrastructure necessary to achieve 100% renewable energy. On this basis, this paper firstly surveys the necessity of using ESSs and DSM strategies in renewable-based distribution networks. Then, the existing policies and incentive programs implemented in different countries for the development of RESs in optimal coordination with ESSs and DSM strategies are presented. The impacts of utilizing ESSs and DSM strategies in improving the economic performance of the renewable-based distribution networks are also investigated. Finally, prevalent energy management strategies, which are proposed to optimize utilization of ESSs and DSM strategies in renewable-based distribution networks, are investigated from the perspective of optimization approaches.
FDDM1 and FDDM2, Two SGS3-like Proteins, Function as a Complex to Affect DNA Methylation in Arabidopsis

DNA methylation is an important epigenetic modification required for the specific regulation of gene expression and the maintenance of genome stability in plants and animals. However, the mechanism of DNA demethylation remains largely unknown. Here, we show that two SGS3-like proteins, FACTOR OF DNA DEMETHYLATION 1 (FDDM1) and FDDM2, negatively affect the DNA methylation levels at ROS1-dependend DNA loci in Arabidopsis. FDDM1 binds dsRNAs with 5′ overhangs through its XS (rice gene X and SGS3) domain and forms a heterodimer with FDDM2 through its XH (rice gene X Homology) domain. A lack of FDDM1 or FDDM2 increased DNA methylation levels at several ROS1-dependent DNA loci. However, FDDM1 and FDDM2 may not have an additive effect on DNA methylation levels. Moreover, the XS and XH domains are required for the function of FDDM1. Taken together, these results suggest that FDDM1 and FDDM2 act as a heterodimer to positively modulate DNA demethylation. Our finding extends the function of plant-specific SGS3-like proteins.
The Synaptic Interactions of Alcohol and the Endogenous Cannabinoid System

PURPOSE A growing body of evidence has implicated the endocannabinoid (eCB) system in the acute, chronic, and withdrawal effects of alcohol/ethanol on synaptic function. These eCB-mediated synaptic effects may contribute to the development of alcohol use disorder (AUD). Alcohol exposure causes neurobiological alterations similar to those elicited by chronic cannabinoid (CB) exposure. Like alcohol, cannabinoids alter many central processes, such as cognition, locomotion, synaptic transmission, and neurotransmitter release. There is a strong need to elucidate the effects of ethanol on the eCB system in different brain regions to understand the role of eCB signaling in AUD. SEARCH METHODS For the scope of this review, preclinical studies were identified through queries of the PubMed database. SEARCH RESULTS This search yielded 459 articles. Clinical studies and papers irrelevant to the topic of this review were excluded. DISCUSSION AND CONCLUSIONS The endocannabinoid system includes, but is not limited to, cannabinoid receptors 1 (CB1), among the most abundantly expressed neuronal receptors in the brain; cannabinoid receptors 2 (CB2); and endogenously formed CB1 ligands, including arachidonoylethanolamide (AEA; anandamide), and 2-arachidonoylglycerol (2-AG). The development of specific CB1 agonists, such as WIN 55,212-2 (WIN), and antagonists, such as SR 141716A (rimonabant), provide powerful pharmacological tools for eCB research. Alcohol exposure has brain region–specific effects on the eCB system, including altering the synthesis of endocannabinoids (e.g., AEA, 2-AG), the synthesis of their precursors, and the density and coupling efficacy of CB1. These alcohol-induced alterations of the eCB system have subsequent effects on synaptic function including neuronal excitability and postsynaptic conductance. This review will provide a comprehensive evaluation of the current literature on the synaptic interactions of alcohol exposure and eCB signaling systems, with an emphasis on molecular and physiological synaptic effects of alcohol on the eCB system. A limited volume of studies has focused on the underlying interactions of alcohol and the eCB system at the synaptic level in the brain. Thus, the data on synaptic interactions are sparse, and future research addressing these interactions is much needed.