Miracle-dz/UniFAR-FTUs huggingface dataset:
Induced ligno-suberin vascular coating and tyramine-derived hydroxycinnamic acid amides restrict Ralstonia solanacearum colonization in resistant tomato roots

The soil borne pathogen Ralstonia solanacearum is the causing agent of bacterial wilt, a devastating disease affecting major agricultural crops. R. solanacearum enters plants through the roots and reaches the vasculature, causing rapid wilting. We recently showed that tomato varieties resistant to bacterial wilt restrict bacterial movement in the plant. In the present work we go a step forward by identifying the physico-chemical nature of the barriers induced in resistant tomato roots in response to R. solanacearum. We describe that resistant tomato specifically responds to infection by assembling de novo a structural barrier at the vasculature formed by a ligno-suberin coating and tyramine-derived hydroxycinnamic acid amides (HCAAs). On the contrary, susceptible tomato does not form these reinforcements in response to the pathogen but instead displays lignin structural changes compatible with its degradation. Further, we show that overexpressing genes of the ligno-suberin pathway in a commercial susceptible variety of tomato restricts R. solanacearum movement inside the plant and slows disease progression, enhancing resistance to the pathogen. We thus propose that the induced barrier in resistant plants does not only restrict the movement of the pathogen, but may also prevent cell wall degradation by the pathogen and confer anti-microbial properties.
Stopping vs Resting state during motor imagery paradigm

Current non-invasive Brain Machine interfaces commonly rely on the decoding of sustained motor imagery activity (MI). This approach enables a user to control brain-actuated devices by triggering predetermined motor actions. One major drawback of such strategy is that users are not trained to stop their actions. Indeed, the termination process involved in BMI is poorly understood with most of the studies assuming that the end of an MI action is similar to the resting state. Here we hypothesize that the process of stopping MI (MI termination) and resting state are two different processes that should be decoded independently due to the exhibition of different neural pattens. We compared the detection of both states transitions of an imagined movement, i.e. rest-to-movement (onset) and movement-to-rest (offset). Our results shows that both decoders show significant differences in term of performances and latency (N=17 Subjects) with the offset decoder able to detect faster and better MI termination. While studying this difference, we found that the offset decoder is primarily based on the use of features in Beta band which appears earlier. Based on this finding, we also proposed a Random Forrest based decoder which enable to distinguish three classes (MI, MI termination and REST).
GIP: an open-source computational pipeline for mapping genomic instability from protists to cancer cells

Genome instability has been recognized as a key driver for microbial and cancer adaptation and thus plays a central role in many human pathologies. Even though genome instability encompasses different types of genomic alterations, most available genome analysis software are limited to just one kind mutation or analytical step. To overcome this limitation and better understand the role of genetic changes in enhancing pathogenicity we established GIP, a novel, powerful bioinformatic pipeline for comparative genome analysis. Here we show its application to whole genome sequencing datasets of Leishmania, Plasmodium, Candida, and cancer. Applying GIP on available data sets validated our pipeline and demonstrated the power of our analysis tool to drive biological discovery. Applied to Plasmodium vivax genomes, our pipeline allowed us to uncover the convergent amplification of erythrocyte binding proteins and to identify a nullisomic strain. Re-analyzing genomes of drug adapted Candida albicans strains revealed correlated copy number variations of functionally related genes, strongly supporting a mechanism of epistatic adaptation through interacting gene-dosage changes. Our results illustrate how GIP can be used for the identification of aneuploidy, gene copy number variations, changes in nucleic acid sequences, and chromosomal rearrangements. Altogether, GIP can shed light on the genetic bases of cell adaptation and drive disease biomarker discovery. One Sentence Summary GIP - a novel pipeline for detecting, comparing and visualizing genome instability.
Coexistence patterns and diversity in a trait-based metacommunity on an environmental gradient

The dynamics of trait-based metacommunities have attracted much attention, but not much is known about how dispersal and spatial environmental variability mutually interact with each other to drive coexistence patterns and diversity. Here, we present a spatially explicit model of competition for two essential resources in a metacommunity on a one-dimensional environmental gradient. We find that both the strength of dispersal and the range of spatial environmental variability affect coexistence patterns, spatial structure, trait distribution, and local and regional diversity. Without dispersal, species are sorted according to their optimal growth conditions on the gradient. With the onset of dispersal, source-sink effects are initiated, which increases the effects of environmental filtering and interspecific competition and generates trait lumping, so that only a few species from an environment-defined trait range can survive. Interestingly, for very large dispersal rates, species distributions become spatially homogeneous, but nevertheless two species at the extreme ends of the trade-off curve can coexist for large environmental variability. Local species richness follows a classic hump-shaped dependence on dispersal rate, while local and regional diversity exhibit a pronounced peak for intermediate values of the environmental variability. Our findings provide important insights into the factors that shape the structure of trait-based metacommunities.
Sirtuin 5 levels are limiting in preserving cardiac function and suppressing fibrosis in response to pressure overload

Heart failure (HF) is the inability of the heart to pump blood sufficiently to meet the metabolic demands of the body. HF with reduced systolic function is characterized by cardiac hypertrophy, ventricular fibrosis and remodeling, and decreased cardiac contractility, leading to cardiac functional impairment and death. Transverse aortic constriction (TAC) is a well-established model for inducing hypertrophy and HF in rodents. Mice globally deficient in sirtuin 5 (SIRT5), a NAD + -dependent deacylase, are hypersensitive to cardiac stress and display increased mortality after TAC. Prior studies assessing SIRT5 functions in the heart have all employed loss-of-function approaches. In this study, we generated SIRT5 overexpressing (SIRT5OE) mice, and evaluated their response to chronic pressure overload using TAC. Compared to littermate controls, SIRT5OE mice were protected against adverse functional consequences of TAC, left ventricular dilation and impaired ejection fraction. Transcriptomic analysis revealed that SIRT5 suppresses key HF sequelae, including the metabolic switch from fatty acid oxidation to glycolysis, immune activation, and fibrotic signaling pathways. We conclude that SIRT5 is a limiting factor in the preservation of cardiac function in response to experimental pressure overload.
Using a Word Analysis Method and GNNs to Classify Misinformation Related to 5G-Conspiracy and the COVID-19 Pandemic

This paper addresses the FakeNews: Corona virus and 5G conspiracy task at MediaEval 2020. The task involves classifying misinformation that is related to conspiracy topics and the COVID-19 pandemic. The task is divided into two subtasks where we for each subtask are proposing a separate approach. The first subtask is a Natural Language Processing (NLP)-based detection task where we are proposing a simple text-based approach by looking at the frequency of words. The second subtask is a structural-based detection task where we are proposing a method using a Graph Neural Network (GNN) to perform classification by investigating spreading patterns. © 2020 Copyright 2020 for this paper by its authors. All Rights Reserved.
FakeNews Detection Using Pre-trained Language Models and Graph Convolutional Networks

We introduce methods for detecting FakeNews related to coronavirus and 5G conspiracy based on textual data and graph data. For the Text-Based Fake News Detection subtask, we proposed a neural network that combines textual features encoded by a pre-trained BERT model and metadata of tweets encoded by a multi-layer perceptron model. In the Structure-Based Fake News Detection subtask, we applied Graph Convolutional Networks (GCN) and proposed some features at each node of GCN. Experimental results show that textual data contains more useful information for detecting FakeNews than graph data, and using meta-data of tweets improved the result of the text-based model. © 2020 Copyright 2020 for this paper by its authors. All Rights Reserved.
Personal Air Quality Index Prediction Using Inverse Distance Weighting Method

In this paper, we propose a method to predict the personal air quality index in an area by only using the levels of the following pollutants: PM2.5, NO2, O3. All of them are measured from the nearby weather stations of that area. Our approach uses one of the most well-known interpolation methods in spatial analysis, the Inverse Distance Weighted (IDW) technique, to estimate the missing air pollutant levels. After that, we can use those levels to calculate the Air Quality Index (AQI). The results show that the proposed method is suitable for the prediction of those air pollutant levels.
Exploring the Programming Concepts Practiced by Scratch Users: an Analysis of Project Repositories

Scratch enables children to learn about programming by creating games and animations, and is currently one of the most popular introductory programming languages. While Scratch has been found to increase students’ motivation and interest in programming, it has been debated whether Scratch users practice and learn about core programming concepts such as loops, conditional expressions, procedures and variables. This paper presents a large scale study of the progression of the programming concepts practiced by Scratch users through an analysis of their complete public project portfolios. A dataset of over 112 thousand authors and their 1 million projects was constructed and analyzed from three viewpoints. First, we investigate the development of programming concepts by looking at block usage statistics for each project in the users’ repositories. Second, we score and analyze the dataset using a computational thinking rubric. Third, we identify users that have left the Scratch platform and evaluate the learning goals they have achieved. Our results show that, while users progress in Scratch, there is a positive trend in the use of all concepts that were examined. Within the least utilized concepts, even after the 20th project of Scratch users, are procedures, conditional loops and logic operations. Examining the users who have left the Scratch platform after creating at least the mean amount of nine projects, we measured that half had left without ever utilizing procedures, and a third had left without ever utilizing conditional loops.
Outage Performance of Relay-Assisted Single- and Dual-Stage NOMA Over Power Line Communications

In this paper, we analyze the performance of relay-assisted, single-stage (SS) non-orthogonal multiple access (NOMA) and dual-stage (DS) NOMA power line communication systems. Specifically, we derive closed form expressions for the outage probabilities of the SS NOMA and DS NOMA schemes. Subsequently, we formulate optimization problems and obtain closed-form solutions for the optimal power allocation coefficients of the SS NOMA and DS NOMA schemes, such that the probability of overall outage is minimized. The accuracy of our analysis and the tightness of the approximations employed are validated through Monte Carlo simulations and numerical techniques. Moreover, we show that the DS NOMA scheme outperforms the SS NOMA scheme, in terms of the overall outage probability.
Work in Progress: Augmented Reality as a strategy to improve learning in engineering

In recent years, the speed of technological advances has forced us to innovate with greater agility. Augmented Reality (AR) has notched an important place in learning due to its increased accessibility to the public and the actors in the educational process. Innovation in education requires using this technology, so academic institutions have shown interest in developing technological applications that support learning processes. There are disciplines in which content development using AR is still an area of opportunity. This work has two purposes: 1) Present an overview of AR's progress and benefits, understood from the professors' and students' perceptions and needs, and 2) present a research proposal in the Operations Research (OR) area for Industrial Engineering students using an educational application of AR. This proposal's research question was: What is the degree of satisfaction of Industrial Engineering students with the Augmented Reality application used in decision-tree practices? This study's findings present a) the students' perception and feedback on Augmented Reality, and b) the need for an interdisciplinary teaching approach,
Measuring Effects of Technological Interactivity Levels on Flow With Electroencephalogram

Although game-based interactive technology has long enhanced flow experiences crucial for learning, its effects have been unclear. Thus, this study gathered students’ electroencephalogram (EEG) information during their work in game-based learning environments with different levels of technological interactivity (LTIs; low, mid, and high LTIs). Multiple measurements were used in a relatively small sample, and 3 9th graders (age 15) of different learning environments worked on 360 test items. The EEG data were analyzed with the LTI, balance of challenge and skill (BCS), and sense of control (SC) to establish the flow state construct. A chi-square test showed a significant association between flow states and the LTI, whereas a J48 decision tree analysis and logistic regression demonstrated that inflow experiences would likely emerge in students with high short-term SC (ST-SC), high BCS, and high-LTI learning environments. Furthermore, in high ST-SC and high BCS cases, the odds ratio (OR) of emerging inflow experiences with a high LTI is eight times more than the rest, suggesting that instructional designers (and teachers) use high-LTI game-based learning environments while ensuring students’ learning with adequate SC and BCS.
Analysis of student motivation in the use of a Physics Augmented Remote Lab during the Covid-19 pandemic

In the current context, amid of the Covid-19 pandemic, technology has become the main means of connecting schools, teachers and students in the educational process. However, not all the tools used are considered clear enough to demonstrate those practical contents, in the same way that they occurred in the pre-pandemic period, which can cause the demotivation of some students. The objective of this investigation is to present the development of an Augmented Remote Lab (ARL) and to verify if such resources are able to motivate students in the acquisition of knowledge of Physics during the period of social isolation imposed by the pandemic of Covid-19. In this regard an ARL was developed and used by students of the technical course in Electromechanics of a Brazilian educational institution for the teaching of basic concepts of electricity, in the discipline of Electronics. From this research, it was possible to identify the motivational levels of students regarding attention, relevance, satisfaction and confidence. The results showed a positive trend in the items of attention, relevance and satisfaction, showing that the ARL has the potential to promote interesting levels of motivation. However, the results regarding confidence remained in a neutral to negative trend, demonstrating that although they are satisfied with learning from ARL, it would not be sufficient to obtain satisfactory results in tests on the topic.
A framework of Malicious Vehicles Recognition in Real Time Foggy Weather

This paper will compare between two frameworks to identify vehicles. The first is a framework that identify malicious vehicles through license plate recognition and communication protocol. The second is a framework that recognize vehicles in foggy weather based on two-step recognition algorithm. The goal is to produce a combined framework of malicious vehicles recognition in foggy weather. The paper will compare between them and extracts conclusions for better combined framework. That framework will combine the positive features of both frameworks in order to recognize malicious vehicles in real time using a camera and algorithms without any human contact. That combination is needed since the outbreak of COVID-19 and the need of least human exposition and contact.
Analysis of Small-Signal Power Oscillations in MTDC Power Transmission System

This paper investigates the small-signal power oscillations in a VSC-based multi-terminal DC (MTDC) power transmission system. Regarding the DC power oscillation modes (DPOMs), this paper demonstrates that when the master–slave control is adopted for the MTDC power system, there is only one DPOM. In contrast, when the DC voltage droop control is adopted, the DC electric power oscillations (DEPOs) may be multi-modal oscillations as there are multiple DPOMs. Furthermore, a better understanding of DEPOs, which is analogous to low-frequency electric power oscillations (LEPOs) in conventional AC power systems, is obtained, unveiling how the DPOMs determine the power oscillations in AC and MTDC power systems. Moreover, this paper theoretically verifies that the DPOMs determine the behaviors of both DEPOs in MTDC systems and power oscillations between the VSCs (which adopt the DC voltage control or DC voltage droop control) and external AC systems. By analogy to LEPOs, damping torque analysis is applied to examine the stability risk of the MTDC power system. The results indicate that even if VSCs are self-stable, there is a potential risk of growing DEPOs occurring in MTDC power systems under heavy DC loads.
Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses

In this publication important aspects for the implementation of inductive locating are explained. The miniaturized sensor platform called Sens-o-Spheres is used as an application of this locating method. The sensor platform is applied in bioreactors in order to obtain the environmental parameters, which makes a localization by magnetic fields necessary. Since the properties of magnetic fields in the localization area are very different from the wave characteristics, the principle of inductive localization is investigated in this publication and explained by using electrical equivalent circuit diagrams. Thereby, inductive localization uses the coupling or the mutual inductivities between coils, which is noticeable by an induced voltage. Therefore some properties and procedures are explained to extract the location of Sens-o-Spheres or other industrial sensor platforms from the couplings of the coils. One method calculates the location from an adapted ratio calculation and the other method uses neural networks and stochastic filters to obtain the results. In the end, these results are evaluated and compared.
Feature-Based Deep Neural Networks for Short-Term Prediction of WiFi Channel Occupancy Rate

Spectrum occupancy prediction is a key enabling technology to facilitate a proactive resource allocation for dynamic spectrum management systems. This work focuses on the prediction of duty cycle (DC) metric that reflects spectrum usage (in the time domain). The spectrum usage is typically measured on a shorter time scale than needed for prediction. Hence, data thinning is required and we apply block averaging. However, averaging operation results in flattening the DC data and losing essential features to assist deep neural network (DNN) to predict the spectrum usage. To improve DC prediction after block averaging, a feature-based deep learning framework is proposed. Namely, long short-term memory (LSTM) and gated recurrent unit (GRU) are selected and enhanced by using features of the data, such as the variance of DC data in addition to DC data themself. The proposed model is capable of proactively predicting the spectrum usage by capturing complex relationships among various input features for the measured spectrum, thus providing higher prediction accuracy with an average improvement of 5% in RMSE compared with traditional models. Moreover, to have a better understanding of the proposed model, we quantify the effect of input features on the predicted spectrum usage values. Based on the most significant input features, a simpler and more efficient model is proposed to estimate DC with similar accuracy to when using all features.
High precision, geometry independent analytical method for self-inductance calculation in planar coils

This paper presents a versatile tool for the self-inductance calculation of planar coils. Due to the growing interest in planar coils in the past few years, the possibility of using an analytical model as a valid alternative to FEM simulations, regarding versatility and result reliability, would be of great interest. The ideal scenario would be to combine speed, precision, easy interaction and understanding, while adding versatility in terms of geometry. To achieve that, a tool, based on Grover equations, that calculates the self-inductance of planar coils with a general geometry has been developed. The results achieved using this method, considering different coil geometries and dimensions, were compared with the main analytical methods that can be found in the literature, proving the reliability of the proposed method. This model has the novelty of not having any limitation on the coil geometry or dimension, which is not the case for the other existing methods.
Through-Wall Human Activity Classification Using Complex-Valued Convolutional Neural Network

Deep learning has attracted intensive attention in human activity classification based on the radar. Whereas, most methods use the images to classify the human activities, ignoring the phase information of the radar data. In this paper, the complex-valued convolutional neural network (Complex-valued CNN) is utilized to classify the human activity behind the wall. We developed several Complex-valued CNN models, which have the same structures as several classical convolutional neural network(CNN) models and use both the amplitude and phase information of the range profiles. Experiments on the real data validate the performance of the Complex-valued CNN models.